A Simple Load Estimation Model

for Rural Electrification in Tanzania

Mikael Andersson

Christina Andersson

Division of Energy Economics and Planning Department of Energy Sciences, Faculty of Technology, Lund University

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Abstract Despite rural electrification being subjected to a great deal of both national and international attention, especially in relation to poverty reduction, less than 1 % of the Tanzanian rural population has access to electricity. The expansion of the grid is impeded by high costs and the poor finances of the national utility, TANESCO. In 1992 Tanesco’s monopoly was lifted and the power sector is slowly undergoing reforms as to allow and encourage private participation [7]. Impact Power Solutions (IPS) is a company in Tanzania whose object is to offer affordable and sustainable power solutions through cost-effective generation and distribution technologies. The aim of the present Master Thesis is to, on IPS’s account, develop a tool that can be used when analyzing the potential electricity consumption in non-electrified villagers in rural and peri-urban areas. The Load Estimation Tool must be easy to use, adapted to Tanzanian conditions and compute the power as accurate as possible. Much effort has been made to define loads and customer types and to find a method that considers how different loads aggregate. During a three months stay more than 100 interviews were conducted with households and businesses in villages mainly around Dar es Salaam, but also in other parts of Tanzania. The interviewees were asked what kind of electric appliances and machines they own, the power of the same as well as at what times of the day they are in use. A model, based on probability theory, for how loads aggregate was elaborated and programmed in the software FileMaker Pro. From the extensive gathered information general loads and customer types could be created. The loads are defined by their power and probability vector, a vector that for each hour of the day display the probability for the load to be in use. The customer types are defined by how many of each appliance or machine they have. These pre-defined loads and customer types were incorporated in the computer program and a user friendly layout was designed. The input to the Load Estimation Tool is the number of each customer type in a village or area investigated for electrification. The most important output is the expected power and the power that will not be exceeded with a certain probability indicated by the α-quantile. The data for each village is automatically saved and the user can chose what villages he wants to include in the calculations. Measurements for a village in Tanzania had been desirable for a model validation, but were unfortunately difficult to come by. Instead values from a Master Thesis performed in Uganda were used. The results indicate agreement between the measurements and the output of the Load Estimation Tool. Keywords: rural electrification, Tanzania, stochastic electric load, aggregation, categorization, estimation model, probability theory

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Acknowledgments We would like to thank the following people for their support: Leif Andersson, Project Manager at Sweco International, Tanesco Sten Bergman, President of StonePower AB Mashauri A. Kusekwa, Head of Electrical Engineering Department at DIT Dr. Richard J.Masika, Vice Principal and Director of Studies at DIT Aggrey F. Mbuya, Sales and Marketing Executive at Triangle Electrocom International Ltd Sithole E. Mwakatage, Project Manager at Impact Power Solutions Ltd Elfrid I. Ng’ombo, Teacher at DIT N. Moses Mwasaga, System Administrator at DIT Tobias Rydén, Professor at the Centre of Mathematical Sciences, Lund Institute of Technology Bengt Simonsson, Senior Instructor at the Department of Industrial Electrical Engineering and Automation Lennart Thörnqvist, Professor at the Department of Energy Sciences, Lund Institute of Technology We would also like to thank Elforsk AB for economic support

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1. Introduction _________________________________________________________ 7 1.1. Objectives______________________________________________________________ 7 1.2. Method ________________________________________________________________ 8 1.3. Structure of the Report___________________________________________________ 8

2. Background _________________________________________________________ 9 2.1. Tanzania_______________________________________________________________ 9

2.1.1. History ___________________________________________________________________ 10 2.1.2. Energy ___________________________________________________________________ 10 2.1.3. Tanesco and the Energy Sector ________________________________________________ 11

2.2. Low Cost Electrification_________________________________________________ 12 2.3. IPS __________________________________________________________________ 13 2.4. DIT __________________________________________________________________ 13

3. Field Studies________________________________________________________ 14 3.1. Field Visits ____________________________________________________________ 14 3.2. Interviews_____________________________________________________________ 14 3.3. Field Knowledge _______________________________________________________ 15 3.4. Account of Our Field Visits ______________________________________________ 16

3.4.1. Sadaani___________________________________________________________________ 16 3.4.2. North West of Tanga ________________________________________________________ 17 3.4.3. Mlandizi__________________________________________________________________ 18 3.4.4. Mbwawa__________________________________________________________________ 19 3.4.5. Masasi and Surroundings _____________________________________________________ 20 3.4.6. Chasing the Permit__________________________________________________________ 21 3.4.7. Mkuranga _________________________________________________________________ 24 3.4.8. Mlingotini ________________________________________________________________ 24 3.4.9. Miono____________________________________________________________________ 25 3.4.10. Mbweni and Kibaha Pi Ya Dege ______________________________________________ 26 3.4.11. Kimbiji and Mjimwema_____________________________________________________ 27 3.4.12. Kisarawe ________________________________________________________________ 28

4. Analysis____________________________________________________________ 29 4.1. Aggregation of Stochastic Loads __________________________________________ 29

4.1.1. Stochastic and Deterministic Loads _____________________________________________ 30 4.1.2. Geometric Addition _________________________________________________________ 30 4.1.3. Probability Theory __________________________________________________________ 36 4.1.4. Conclusion ________________________________________________________________ 37

4.2. Loads ________________________________________________________________ 37 4.2.1. Security Light______________________________________________________________ 39 4.2.2. Lights ____________________________________________________________________ 40 4.2.3. Radio ____________________________________________________________________ 41 4.2.4. TV ______________________________________________________________________ 42 4.2.5. Fan ______________________________________________________________________ 43 4.2.6. Iron______________________________________________________________________ 44 4.2.7. Fridge/Freezer _____________________________________________________________ 44 4.2.8. Mill______________________________________________________________________ 45

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4.2.9. Electric Engine_____________________________________________________________ 46 4.2.10. Weld____________________________________________________________________ 48 4.2.11. Charger Car Battery ________________________________________________________ 48 4.2.12. Pump Filling Station _______________________________________________________ 49 4.2.13. Razor ___________________________________________________________________ 49 4.2.14. Stand Alone Hairdryer ______________________________________________________ 50 4.2.15. Amplifier/Loudspeaker _____________________________________________________ 50 4.2.16. Big Health Appliance_______________________________________________________ 50 4.2.17. Small Health Appliance _____________________________________________________ 51 4.2.18. Video ___________________________________________________________________ 51 4.2.19. Photocopier ______________________________________________________________ 51 4.2.20. Computer ________________________________________________________________ 52 4.2.21. Printer___________________________________________________________________ 52 4.2.22. Sewing Machine___________________________________________________________ 53

4.3. Reduced Power ________________________________________________________ 53 4.4. Customer Types________________________________________________________ 56

4.4.1. Households________________________________________________________________ 56 4.4.2. Shop _____________________________________________________________________ 60 4.4.3. Bar/Restaurant _____________________________________________________________ 61 4.4.4. Hairdresser Men and Hairdresser Women ________________________________________ 61 4.4.5. Tailor ____________________________________________________________________ 62 4.4.6. Mosque___________________________________________________________________ 63 4.4.7. Church ___________________________________________________________________ 64 4.4.8. Mill______________________________________________________________________ 64 4.4.9. Carpentry _________________________________________________________________ 64 4.4.10 Workshop ________________________________________________________________ 65 4.4.11. Filling Station_____________________________________________________________ 66 4.4.12. Dispensary _______________________________________________________________ 66 4.4.13. Health Centre _____________________________________________________________ 67 4.4.14. Guesthouse_______________________________________________________________ 68 4.4.15. Video Show ______________________________________________________________ 69 4.4.16. Secondary School__________________________________________________________ 70 4.4.17. Office ___________________________________________________________________ 71 4.4.18. Secretary Service __________________________________________________________ 71

5. Load Estimation Tool_________________________________________________ 72 5.1. FileMaker Pro _________________________________________________________ 72 5.2. Computer Program_____________________________________________________ 72 5.3. Model Validation_______________________________________________________ 73 5.4. Socio-economic Analysis_________________________________________________ 76

6. Discussion and Future Work___________________________________________ 79

7. References _________________________________________________________ 80

Appendix_____________________________________________________________ 81 A Interviews ______________________________________________________________ 81 B Interviews by Blennow [13] ________________________________________________ 81 C Calculations for Loads____________________________________________________ 81 D Observations by Appliance ________________________________________________ 81

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E Week Variations _________________________________________________________ 81 F User Guide______________________________________________________________ 81

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1. Introduction

1.1. Objectives

Load Estimation Tool The main objective is to design a Load Estimation Model and a Load Estimation Tool that can be used to analyze the potential electricity use in non-electrified villages in urban and peri-urban Tanzania. The Load Estimation Tool is a computer program, which is based on the Load Estimation Model that we have developed. The input to the program is the number of each customer type, e.g. number of households, number of shops etc. The output is a vector of expected power for each hour. The Load Estimation Tool must compute the power as accurate as possible. Much effort has been made to define loads and customer types and to find a method that considers how different loads aggregate. The computer program must be easy to use and adapted to Tanzanian conditions. It must also be flexible in the way that it can be used for areas with different degrees of economic development.

User Guide A user guide should be enclosed with the Load Estimation Tool. It should contain instructions on how to enter data and how to execute the program as well as adding new loads and customer types.

Presentation Knowledge transfer is an importance part of the Master Thesis. This is done through a presentation for key stakeholders in the project. Both the idea of low cost electrification and the advantages of the computer program will be put forward.

Socio-economic Study The last objective is to visit a few pilot sites that are of interest for electrification in order to collect input data to the Load Estimation Tool. This information will used to test the user-friendliness of the computer program.

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1.2. Method The main objective and most time consuming part of the Master Thesis has been to develop the Load Estimation Tool. To reach this objective our methodology has been as follows: We have visited electrified villages in rural and peri-urban areas where we have interviewed the inhabitants about their electricity consumption. We have systemized the data in such a way that all loads and customer types (e.g. household, shops etc.), likely to be encountered in rural Tanzania, easily could be modelled. We have determined power and usage pattern for each load and number of loads for each customer type. This information has then been entered into the computer program. We have discussed and evaluated two methods on how to handle the mathematics of the aggregation of the loads and customer types. The chosen method has been entered into the code of the computer program.

1.3. Structure of the Report The setting of this Master Thesis is rural Tanzania. Some knowledge of this environment is of great importance for the reader, who is not only interested in how the electricity consumption is characterized, but also why. In the next chapter is presented a general description of Tanzania as well as a more into depths account of low cost electrification, the motive for this Master Thesis. In chapter three we have described how the collection of data was carried out, arrangements for field visits and how interviews were conducted. We have also included a narrative account for our field visits that can be skipped by the reader without any loss of technical information - but it is fun to read! Chapter four is the core part of the Master Thesis. It contains the theory for how the loads are aggregated in the computer program as well as how loads and customer types are defined. For each load and customer type there is a brief introduction, to get a general idea of its character and cultural framework, and a presentation of the technical data. Chapter five focuses on the computer program that we have developed and its software, FileMaker Pro. Here is also found an evaluation of the outcome of the program and a demonstration of how it can be used. The outcome of the computer program is discussed in the last chapter. We have also put forward some proposals for how it can be further developed and improved.

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2. Background

Even though there is a strong correlation between income and energy consumption it is not until recently this issue has been given any attention in relation to poverty reduction. Individuals with a low income cannot afford to use a lot of energy, but if the reasoning is reversed then economic growth is not achievable without access to energy. To reach the eight UN Millennium Development Goals1 it is absolutely vital that people in developing countries have access to modern technology and a greater quantity of energy. A shift from a traditional to modern energy use is a prerequisite for better health care, higher education, increased information flow and other tools for poverty reduction and increased economic growth [11].

2.1. Tanzania Constitution: republic, democracy Largest city: Dar es Salaam Area: 945 000 km2 Official languages: Kiswahili, English Population: 33 million (2002) Religion: Islam, Christianity and traditional African religions

Figure 2.1. Map of Tanzania

1 1. Eradicate extreme poverty and hunger. 2. Achieve universal primary education. 3. Promote gender equality and empower women. 4. Reduce child mortality. 5. Improve maternal health. 6. Combat HIV/AIDS, malaria and other diseases. 7. Ensure environmental sustainability. 8. Develop a global partnership for development [12].

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Tanzania is situated (just) south of the equator on the east coast of Africa. It is a magnificent country in terms of wildlife and geographical features. Both Africa's highest mountain, Kilimanjaro, and about half of Africa’s largest lake, Lake Victoria, is located in the north of Tanzania [2]. It has 12 national parks, among them the well-known Serengeti, and a wide variety of animals and vegetation [1]. The population is large but unevenly distributed, thus there are vast rural areas which are sparsely inhabited [2]. About half of the population is living below the poverty line [1]. After the independence in 1961 Tanzania became a socialistic one party state and it was not until ten years ago that other parties were allowed to take part in the election. The next election is to take place later in 2005 and some problems like clashes and riots are anticipated.

2.1.1. History

Since 1964 the two former sovereign states, Tanganyika and Zanzibar, constitute The United Republic of Tanzania [1]. The mainland of Tanzania has since pre-historic time been an area of settlement and a meeting point for different people. This is why there exist no isolated tribes and therefore Tanzania has experienced less ethnical tension and conflicts then many other countries in Africa [2]. The coastal areas were early involved in trading with Arabia and this influence remained until late 15th century when the Portuguese arrived. During the years between the arrival of the Portuguese and the colonisation of Tanganyika by the Germans and of Zanzibar by the English in the late 19th century, the areas were mainly controlled by the kingdom of Oman. During the First World War Britain advanced into Tanganyika and the whole country was eventually occupied. Under the Treaty of Versailles (1919) Britain was entrusted with a mandate, by the League of Nations, to administrate the territory of Tanganyika [1]. Although economic development under British rule was not very successful, the political process improved. When Tanganyika attained independence in 1961, Julius Nyerere, winner of the election the previous year, initially became prime minister and then president. In 1963 also Zanzibar became independent and in 1964 The United Republic of Tanzania was formed. Nyerere, today one of Africa’s most well known and respected characters, was a driving force in uniting the country and in introducing a socialistic economy, with focus on rural development [2]. Unfortunately, Tanzania has, as so many other African states, encountered severe setbacks in forms of immense debt burden, collapse of commodity prices, oil shocks and droughts [1]. These problems together with the socialistic regime, that has led to great achievements in areas of education and social standards, but unfortunately has not raised the economic standard, has left Tanzania still one of poorest countries in the world [2]. Since 1981 several economic reforms have been implemented and today Tanzania is on its way towards liberalization of the economy and privatisation of formerly Government owned enterprises, closely monitored by the IMF. At present Tanzania is experiencing significant economic progress; fiscal balance is positive, inflation is controlled and foreign reserves have increased [1].

2.1.2. Energy

Tanzania has substantial and diverse indigenous energy resources, but only a small part is utilized. 92 % of the energy consumption comes from wood fuel and charcoal and this has led to, among other things, deforestation [1]. About 1 % of the consumed energy is electricity and the bulk of it is hydro power. The full potential for hydro power is estimated to 3,800 MW, but only around 577 MW is installed. Hydro power is a good energy source from an economic and environmental point of view, but has an inherent disadvantage in its dependence on weather and drought often causes severe problems [4]. Imported diesel and petroleum is used for transport and generators, but is hoped to partly be replaced with natural gas, which has been discovered and is continuously explored,

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both on- and off-shore [3]. Figures differ to some extent depending on which source that is referred to, but Tanzania has a thermal power capacity of about 236 MW [9]. It was estimated in 1990 that about 8 % of Tanzania’s population had access to electricity. Unfortunately it has since declined to 7 % (1999). For rural electrification the figure is much lower, less than 2 %, despite this issue being subjected to a great deal of both international and national attention [7]. In addition to the 794 MW of power that is installed in Tanesco’s2 national grid, 40 MW, mostly diesel power station, supply local grids in isolated regions [9]. The main objectives for rural electrification are to curb deforestation by substitution of wood fuel with electricity and to boost small industries. But the progress is slow as the main utility, Tanesco, is under hard economic pressure and rural electrification is a large financial burden [4]. The state of the national grid is poor.

2.1.3. Tanesco and the Energy Sector

Tanzania Electric Supply Company Limited (Tanesco) was established in 1964, three years after independence, when the Government bought all shares in the two existing private power companies and merged them into one utility. Today Tanesco is responsible for almost all generation, transmission and distribution of electricity to the Tanzania mainland and also sells bulk power to Zanzibar State Fuel and Power Corporation, which distributes the power on the islands Zanzibar and Pemba. The operation of Tanesco is regulated by the Ministry of Energy and Minerals and all members of the Board of Directors are appointed by the Minister for Energy and Minerals. The Chairman is appointed by the President [5]. Tanesco is struggling with a series of problems. Some of these are: long time lapse between electricity consumption and billing, non-paying customers, tariffs that do not cover costs, low coverage and inability to finance further investments [10] as well as overstaffing and poor reliability of supply [9]. In recent years the power demand has grown and Tanesco has failed in providing more power leading to shortages and blackouts, which impede both Tanesco itself and economic development of businesses and industry [10]. Worldwide there is a growing conception that the private market is better fit, than the public sector, to handle the current shortcomings in developing countries. There is an ongoing trend towards liberalizing of the energy sector and due to pressure from international donors Government of Tanzania have decided to undertake several reforms to restructure the energy market [10]. These reforms consist of changes in the law, changes in power sector policies, commercializing of Tanesco and the start up of various authorities responsible for regulating and controlling the market [9]. Even though private participation might increase available power to a lower cost it is unlikely that it on its own will solve the problems with rural electrification. Therefore as a major part of the National Energy Policy formulated in 2003 the Rural Energy Agency (REA), Rural Energy Fund (REF) and Rural Energy Board (REB) have been established. The REA’s main objective is to promote new investments in modern energy to increase availability to electricity and heat for people in rural areas. It will find “key partners” among Government agencies, the local Government, the private and financial sector as well as NGOs to work with in partnership identifying energy projects that will benefit rural regions. The REA will assist in realizing these projects by providing training and technical support as well as managerial and financial assistance. Funds from the REF will be used to co-finance appropriate projects to lower the capital costs. This will reduce investor risk and the final cost for the consumer. The main purpose of REB is to oversee

2 Tanzania Electric Supply Company Limited, the national energy company.

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the activities of REA and to debate and approve proposals prepared by the REA on different projects. It will also direct REA on disbursement from the REF [9]. Already in 1992/93 when the new Public Corporation Act (replacing the old Public Corporation Act of 1969) eradicated the monopolistic nature of organisations owned by the Government the first steps towards an eventual privatisation of Tanesco was taken. In year 2000 the preparations for selling parts of Tanesco officially started. Three main tasks had do be executed: 1) Separation of commercial electrification from social/rural electrification, 2) Vertically separate Tanesco into three companies responsible for generation, transmission and distribution, 3) Establishment of an independent regulator. However, a decision on these issues is still to be taken by the Government [9].

2.2. Low Cost Electrification As mentioned above the importance of rural electrification has been recognized for decades and many electrification programs have been undertaken, but as the population is growing faster than the connection rate the number of connected people expressed as a percentage is still decreasing. The slow connection rate is due to high costs extending the national grid over vast areas that are scarcely populated and high costs for local small scale electricity generation. A study, comparing the costs for rural electrification, shows that there are big differences in costs between countries and consequently there is a great potential for savings. A sustainable least-cost strategy that does not result in downgraded performances, higher maintenance burdens, lower electrical safety or severe power limitations needs to be implemented [8]. So how can this be done? Many developing countries use European (3-wire) or North American (4-wire) urban standards, often capable of transferring many MW of power even though the real load is very much lower. A striking example is the transmissions lines in Burkina Faso, which were built to withstand ice loads according to French specifications. Using these standards makes the electrification very expensive and this might in many cases be an unnecessary cost as appropriate design and planning can reduce costs tremendously. One way, that supplies millions of people all over the world with electricity, but still not very well known by the national utilities, project developers and donors, is to use single-phase only. Using single phase instead of 3-phase leads to significantly lower expenses for conducting material. In addition, reducing the conductor area makes it possible to increase the pole span and thus minimizing the number of poles and insulators, stay wires etc. Single-phase distribution generally report less failures as a result of a lower number of critical components and decreased mechanical stress. This will give a total reduced cost on material, construction and maintenance. Single-phase distribution can be done in at least two ways, either by bringing out two conductors instead of three or to only bring out one and then use the earth as a return conductor: Single Phase Earth Return (SWER) system (author’s note: probably means “Single Wire Earth Return”). This would be the simplest way of electrifying underdeveloped areas, but is limited to shorter distances (e.g. 500 kW up to 25 km with an aluminium conductor of 75 mm2) and to areas where the soil is of good conductivity. There has been though a problem with earth conductors being stolen or vandalized leading to power cuts and concern for personal safety [8]. In the planning phase the electrical load is often over estimated. The time horizon for load growth is up to 25 years which is too long, as even detailed and well prepared estimations often turn out be inaccurate. The usual way of linearly adding loads (the maximum power) without taking any statistical model (that not all loads are on at the same time) into account also give a higher value. Further adding to 3-phase being seen as

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“the only” way, is the limited knowledge that for productive users there are large single-phase motors, up to 60-75 kW, available [8]. Another important factor for reducing costs is to use locally available material and components. This is in particular true for poles and some pole-top assembles. Poles constitute as much as 20-30 % of the cost and even more if they have to be transported a long way. Ideally, these could even be supplied by the villagers themselves [8]. Even labour could be contributed by the consumers. Labour costs also make up about 20-30% of the total cost and becomes proportionally higher if material costs is reduced. The kind of work that the consumer could do himself is for example digging cable trenches and building transformer houses [8]. Underground cable has always been thought of as a more expensive alternative than over head lines, but these days the cable can be extruded which is a much faster and cheaper manufacturing process. If the customer in addition can assist with the excavation this might be by far the less costly option. [15] Many electrification programs have been financed through bilateral donors and costs have then not been considered as the limiting factor and little has been done to develop more conservative Load Estimation Models. Awareness among donors, project developers, national utilities and private business about these issues needs to be risen and appropriate energy planning models for low cost designs for low load rural areas need to be elaborated [8].

2.3. IPS IPS is short for Impact Power Solutions Ltd, Tanzania. It is partly owned by StonePower AB, Sweden. It is the president of StonePower AB, Sten Bergman, who has initiated this Master Thesis [15].

IPS is active within Rural Development. One of the objectives is to introduce low cost electrification in Tanzania by using low cost equipment, but projects of supplying information and communication technologies are also a part of the agenda [15]. IPS has provided us with assistance and transport during our work in Tanzania and we are most obliged.

2.4. DIT We were invited to Tanzania by Dar es Salaam Institute of Technology (DIT) and its Electrical Engineering Department to perform our Master Thesis. DIT was established in 1997, but has its origin in a technical institute going way back. Its major function is to provide facilities for studying, training and research [16]. The staff at DIT has been most helpful and provided us with assistance, transport and a place to work. We are most grateful for all their help.

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3. Field Studies

3.1. Field Visits The main reason for our three months stay in Tanzania was to conduct interviews with people living in rural areas. We have made over 100 (116) interviews during twelve successful field visits. During some of these field visits we have visited more than one village and there have also been visits where no interviews at all have been achieved. Altogether we have talked to people in more than 20 communities around Tanzania. Most of these have been situated in the surrounding area of Dar es Salaam, but we have also made longer trips over night both north and south of Dar es Salaam. On these trips as well as when we have travelled for leisure we have stopped for breaks in numerous rural settlements. Always on the look-out for interesting observations even this has added to our knowledge of rural and peri-urban electricity use in Tanzania. It is a complicated matter communicating with and getting the confidence of the villagers. An interpreter, who has carefully explained who we are and what our intentions were, has been absolutely necessary. We have also needed transport to all villages. DIT and IPS provided us with this and we were always accompanied with an interpreter and a driver. Accounts of each field visit can be found in chapter 3.4. The gathered information must be appropriate for the general customer types and the general loads we wanted to create. Therefore we carefully picked the sites we wanted to visit. These were usually situated in electrified areas, but even some non-electrified settlements have been visited. In non-electrified areas the interviews have predominantly dealt with the use of kerosene lamps and battery operated appliances along with milling machines run on diesel. The user times we have asked for have been treated equally irrespective of the area being electrified or not. The questions that have been posed can be found in the next chapter. All interviews are presented in appendix A. They are numbered after which of the twelve field visits they originate from along with a consecutive number. E.g. (6:3) is the third interview in the sixth field visit (Mkuranga 13/4).

3.2. Interviews For domestic users the principal question has always been what electrical appliances they have, in what number and when they are used. Furthermore, we have sometimes asked about the power of the appliances and if the usage varies over the week or over the year. For commercial users like shops and bars there has been additional questions like when they are open and if they experience any changes due to seasons. To get a grasp on how big this variance might be we have asked how many customers they have per day for

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each season. Sometimes other measurements of activity than the number of customers have been used. For productive users like carpentries and mills where electric engines are present the need for further investigation has generated more questions (and expressions) that the reader has to familiarize himself with. The first is the rated output. To understand the stochastic behaviour of the engine we needed to know how many times per day the engine is turned on and off and for how long it is kept running on an average. For this we introduced the expressions: No. of continuous sequences per day Length of one continuous sequence This is sometimes not enough information on the behaviour of the engine. After the power is turned on, let us say for a milling machine, there is yet another level of power pattern. The milling machine is either milling or running without load, waiting for next customer to put his maize in the funnel. We call this with load and without load. The time when it is actually milling (with load) we call a process. To learn about this we asked for: No. of processes per day Length of one process It is also essential to know the total energy used by the engine during a day. This can of course be calculated from the information above, but since the questions can be difficult to explain we have also asked for: Total time of use per day Posing this additional question, which gives us information we already have asked for, is also a way of double checking all answers. If it has been possible we have also made measurements during the interviews. The measurements have usually been of the current both with load and without load, but we have sometimes also timed the processes.

3.3. Field Knowledge Since many days during our three months stay in Tanzania were spent in rural villages talking to people and watching them in their every day life we have an extensive knowledge of rural electricity use. We call that our field knowledge. Sometimes, when we have not enough information about a load or customer type or when we have gotten a result that we believe is incorrect we have referred to this field knowledge and adjusted the values.

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3.4. Account of Our Field Visits In this chapter we have written about our experiences and adventures when we were out on field trips to the rural villages around Dar es Salaam and elsewhere gathering information. We have not included any technical details, thus this chapter can be skipped by the reader who is only interested in the technical part of this Master Thesis. We have tried to write about our experiences in a humorous way. Instead of a dry and correct text we have written with emotion and most often with a twinkle in our eyes. The main characters in these stories are: Mr. Elfrid Ng’ombo, teacher at Dar es Salaam Institute of Technology (DIT) and our guide and translator on the field trips arranged by DIT. Mr. Sithole Mwakatage, Project Manager at Impact Power Solutions Ltd (IPS). Mr. Aggrey F. Mbuya, Sales and Marketing Executive at Triangle Electrocom International Ltd, a co-operation partner to IPS in certain projects.

3.4.1. Sadaani

17/3 Field Visit 1 Written by Christina and Mikael Despite of Africans being notoriously late, our first field visit set off only 45 minutes after scheduled time. Aggrey and Sitole from IPS picked us up in a 4WD outside our accommodation at the university area. We were all going to collect information, Aggrey and Sitole for IPS and we for our Master Thesis project. After a quick stop to pick up Aggrey’s posh wife, who was joining us for the weekend, we left the bustling streets of Dar es Salaam behind and headed for the rural areas around Sadaani Game Reserve, a couple of hours drive north of Dar. Experiencing the Tanzanian country side gave rise to mixed feelings. We were astonished at the splendid nature as well as distressed by the people’s poverty. An hour later we left the smooth asphalt road and started the bumpy ride towards our first rural village, Manda, that we later understood was the poorest site we were going to visit. Walking the streets of Manda seemed unreal, like watching a television program on developing countries, but we were actually in it. The tiny houses were made out of clay, coloured red from Africa’s red soil. Roofs of thatch3. Doors and windows were simple holes in the walls.

3 straw or reeds used to make roofs

Figure 3.1. Stuck in the mud

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Figure 3.2. The company going to Tanga

When a visitor enters a village he must always first see the Chairman to inform him of his intensions. If the Chairman consents the visitor is then required to sign the guestbook. After doing this we could ask the Chairman our questions. Since this village was a potential site for electrification our main purpose was to collect data for a socio-economic analysis. For this we wanted to know for example the size of the village, degree of development and what energy sources they use. Leaving the Chairman’s office a group of about 15 women in beautiful coloured dresses passed by, singing typical African songs smiling at the two surprised Mzungus4. Changing from thatch roof to iron roof is a priority for most households when income increases. At our second stop, Mkange, we saw quite a few more houses with iron roof and therefore assumed this village to be wealthier than Manda. The procedure of signing the guestbook was identical to the one in Manda. Sadaani, an old fishing village, and the third place for us to visit, was situated right on the coast inside the Sadaani Game Reserve. Here you can spot hippos, crocodiles, giraffes, and maybe even a lion or an elephant. Unfortunately we did not see any, not even the smallest monkey or an exotic bird. Even though Sadaani village once was one of the major ports in the area, not much of its former glory remains. Leaving Sadaani village about one o’clock, exhausted from not having anything to eat since breakfast at 7 am, we were starting to wonder if Tanzanians did not eat lunch. We were on our way to a nearby salt factory that was a potential customer to be electrified. The road to get there was in really bad condition and soon we were stuck in the mud. Even though the rainy season had not started yet, it took us two hours and some help from a few kind villagers to get our car back on to the road. Choosing white high healed shoes that morning Aggrey’s posh wife did obviously not expect an adventure like this. Since we lost some hours struggling with the car we had to cancel the rest of the day and headed for the city of Tanga where we were going to stay the night. There exist no streetlights and it was dark, the road was narrow and Tanzanians drive without fear of life. Although we were very tired, every time we overtook another car we became wide awake.

3.4.2. North West of Tanga

18/3 Field Visit 2 Written by Christina and Mikael Revitalized from a good nights sleep we set of early in the cool morning hours. Our goal this day was to visit some mines north-west of Tanga and the non-electrified villages in the area. At our first stop, Daluni, some of the villagers seemed very enthusiastic about IPS’s electrification project. One of the villagers even joined us for the trip around the area.

4 The commonly used word in Swahili for Caucasians meaning: “the white man that wanders about, doing nothing”

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Their village, as well as Kalalani that we also visited this day, was heavily dependent on mining for precious stones. Since the mines provide employment opportunities the region seemed a little wealthier than the area around Sadaani. In Kalalani we met a mine owner that showed us to his mine and who also took us around to some of the other mines in the area. It is difficult to imagine worse working conditions than those for the workers in the mines. In the bigger mines they had some machines, but at the smaller sites everything was done by hand. Deep inside the holes the rock is worked upon by pick axe and the stone is carried up to the ground. Under the scorching sun the rocks are then crushed so that the precious stones can released. The work is probably also very dangerous. At one of the mines a flock of goats suddenly appeared. Perplexed we watched them approach and at first we thought they were falling down the steep walls. Relieved, we then understood that what we thought of as an uncontrolled fall was actually an intended rush for the water at the very bottom of the pitch. To our surprise the last site visited turned out to be managed by a Mzungu named Peter, a Canadian with Swedish (Gävle) heritage who was equally surprised, but pleased, by our appearance.

3.4.3. Mlandizi

23/3 Field Visit 3 Written by Christina and Mikael We met up with the ever smiling John, our driver for the day who we got to know when he picked us up at the airport, and Mr. Ng’ombo who was going to be our interpreter and guide. Also this field visit seemed to start without any longer delay. However, this turned out to be false hopes. Before hitting the main road we had to fill up at a special filling station, for Government vehicles only. This filling station was only opened on Mondays, Wednesdays and Fridays. Today was Wednesday and since the coming Friday and Monday were Easter holidays the cues were very, very long and tremendously slow. It seemed like cars where cueing from all directions. Drivers were leaving their cars, cars were cutting in from of each other and through our Swedish eyes it all looked like complete chaos. After an hours and a half we could leave the chaos behind and continue on our way. Finally arriving at our destination, the village of Mlandizi, Ng’ombo took us to the Chairman’s office, but the Chairman was not present. Instead we spoke, for quite some time, to his secretary who provided the guest book for signing. According to our experience this meant that we were officially approved and could start our visits. Leaving the house we met the Chairman and stopped for some polite chat. After two conversations with important people things had to be all set, had they not? But as it turned out we were actually on our way to the local Government. Here sat two men with an aura of great authority who listened carefully to our story, told for the third time by our Mr. Ng’ombo. We signed the book and were then sent to the next and final hierarchic level. Of special delight to the female author, God was a woman. For the forth time our story was told and once again a guest book was signed. She told us to go back to the local Government where a permission finally could be received. Our mission this day was to visit households and gather information about domestic loads. After the slow start when wondering if any work would ever be accomplished things suddenly moved smoothly and ten households could be interviewed. Mlandizi village is situated along the main road westward from Dar es Salaam and is much wealthier than the villages we saw during the two earlier field visits. Hence, the houses we visited were all much better equipped than we had expected. Our visit was always of

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Figure 3.3. Elderly man demonstrating a kerosene lamp

interest for everyone in the household and wherever we went there was a horde of children following us. Two little girls were so curious of the two white persons they could not resist touching Mikael’s hand and when it was time for a picture all children were so excited they could not stand still. And there were loads of children. One mother even had to count them when we asked how many she had.

3.4.4. Mbwawa

30/3 Field Visit 4 Written by Mikael I was going without my companion on this field trip as Christina unluckily had a tooth break and needed to visit a dentist. After the thirty minute walk from our hostel into town in the already blazing morning sun, I found Mr. Ng’ombo waiting for me in his office. The driver John, smiling of course, was cleaning the car as we came around the corner to the parking lot. On our way home from our last field visit we had made a stop at the small non-electrified village of Mbwawa to ask for permission to conduct interviews. But the Chairman wanted some time to prepare for our visit and we made arrangements to return to Mbwawa the next Wednesday. Returning to Mbwawa this Wednesday the procedure of talking to the Chairman and getting started was very quick. We did not even have to sign the guestbook. I told Mr. Ng’ombo I wanted to visit about ten households and ask them about their usage of lighting and battery-powered appliances. Mbwawa is a smaller village than Mlandizi and situated far away from the main road. Many of the villagers seemed to enjoy telling me about their use of kerosene and batteries and some of them proudly demonstrated their larger kerosene lamps. Mr. Ng’ombo later told me that the possession of a kerosene lamp of size was a sign of wealth. Some of the households had only a few kerosene lamps of the smallest size and those households also appeared to be the poorest. I was also shown the village’s water pump, which was running on solar power. The installation was impressive and working very well apart from during the wet season, when the solar panels did not supply enough power. The village was, according to the inhabitants, soon to be electrified and the water pump was the highest priority. As it had been a very hot day I was relieved when around noon some heavy clouds hid the sun and there was finally some rain. Mr. Ng’ombo had classes later that day so we had to return home in the early afternoon.

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Figure 3.4. On the way to Masasi

3.4.5. Masasi and Surroundings

7/4-11/4 Field Visit 5 Written by Mikael Aggrey was planning to go on a business journey to Masasi, in south eastern Tanzania close to the border of Mozambique, and asked if we wanted to come along. The aim was to make a socio-economic analysis of some villages’ potential to get electricity from a biogas project. As we wanted to make as many field visits as possible we decided that I would go and Christina would stay in Dar es Salaam and continue working with DIT. Arriving at Ubungo bus station, eight km west of Dar es Salaam city centre, at five o’clock in the morning in pitch darkness I understood what being a Mzungu really means. I was shouted at from all directions. Did I need safari arrangements? Or perhaps a bus tickets to various tourist attractions? I kept telling them that I already hade a ticket and was just waiting for my colleague. Twenty-five minutes and two disconnected telephone calls later, Aggrey finally arrived. We boarded the bus and I was very happy for my seat next to a window. Apparently busses in Tanzania have five seats in a row instead of four and still take standing people. Aggrey bought an extra five litres of drinking water and new batteries to his torch. He told me you never know what could happen. Not far away from Dar es Salaam the paved road came to an end. I hade been told that the trip to Masasi could be really rough. If we were lucky we should arrive within 14 hours. Sometimes the trip lasted for two days. I watched the beautiful landscape and felt quite comfortable considering the state of the road and vehicle. I was happy I decided to come along. The hours went by and the sun set behind the horizon of the Tanzanian countryside. Finally, at 11 pm we arrived in Masasi after seventeen hours and Aggrey said that this was still good considering it was rain season. I was amazed by how people took it with ease to stand throughout the whole rough trip. I admire the Tanzanians for their strength standing throughout the whole trip and I was amazed by how people took it with ease; they just kept on smiling for 17 hours. The next day we got up early, arranged for transport and went to a big cattle farm. The dung from the cattle was to be used to generate biogas for electricity and IPS was the company engaged to supply the solution. We talked to the farmer and then paid a visit to the surrounding villages where I could collect data for our project. The following day I was free as Aggrey was doing some work of his own and I decided to explore Masasi village. This are of Tanzania is isolated due to the poor communications and white people are rare. On the contrary to what one would expect I attracted less attention here than in Dar es Salaam. Aggrey explained to me that if a white person actually does come here he must be travelling on business and is therefore treated with much more respect than the tourist running around in Dar es Salaam.

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Since his work could be prolonged a couple of days we decided that I was to go back on my own the next day. At first I thought it would be impossible; anything could happen, but after thinking a while I agreed. I equipped myself with three litres of water and some cookies. Early in the morning, at 4:30 am, Aggrey followed me to the bus stop to make sure I got on the bus. The bus arrived and we left for Dar es Salaam. Once again I got a good seat next to a window, but this is where my luck ended. As I said, anything could happen and it did. Suddenly, coming to a hill the bus stopped and we hade to walk all the way up. Going up the next hill the bus came to another stop. And began rolling backwards. It did not stop rolling. One of the staff jumped out of the bus trying to put a ‘stopping log’ under the wheel, but the bus had gained too much speed already and just rolled over it. I was starting to get really frightened as the bus driver turned the bus into the ditch to get stop of it. He succeeded though. A truck passing by helped us getting onto the road again. At the top of the hill, in the middle of nowhere, everybody went out of the bus and sat down waiting. As I did not know anyone, nor spoke Swahili, I did not understand much. I tried to ask one of my fellow passenger, but I was not able to get much information. I sat down with some people under a tree next to a clay house. The family was preparing their harvest of maize. Next to me sat an elderly Arabic man. He had brought his radio and I noticed he was listening to an English channel. Later I talked to him and he was well spoken and could explain what was going on. There was something wrong with the transmission of the bus and two guys of the staff had left for the nearest town with another ride. It should take another three hours maximum. Alright, I thought, even if it becomes five hours I’ll be fine. As the hours passed by and nothing seemed to happen rumours started go around about a new bus being sent for us. I did not know what to believe and began to get frightened. What if no bus comes today, how long will my water last? As the sun set I realized how unprepared I was. There is nothing as dark as the African night and I had no torch. I went to my seat with my bag in my lap and thought I could stay there until the sun rose again. Finally, after ten hours of not knowing anything, a new bus arrived. After some struggle I got my old seat back and I felt much relieved to be moving again. The rest of the trip went well and I arrived back in Dar es Salaam after thirty two hours of travelling.

3.4.6. Chasing the Permit

Written by Christina and Mikael Without having experienced it yourself it is very difficult to image the culture chock of coming to a society so extremely different from your own. In most cases it was a wonderful adventure and an exciting challenge, but there were also times when we thought we were going insane. The most difficult part to handle was the tedious bureaucracy and inefficiency that was a part of all Governmental institutions. Tanzania is a young democracy and this year there are to be held a general election, for the third time ever. This will probably mean some clashes and the political climate is somewhat tense. We were told this is one of the reasons for all the trouble we got in one area where we wanted to conduct interviews. Here below is brief summary of how we for over a month were chasing a permit that would allow us to conduct interviews in the Bagamoyo district, north of Dar es Salaam. The reader should keep in mind that the places, like Kibaha and Bagamoyo, that we mention in this text are all situated at least one hours drive away from Dar es Salaam. 1/4 Friday Went to Bagamoyo, this time to the Regional Commissioner instead of a Chairman. He sounded very interested, but concluded that we needed a letter from a very important

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man who we, in spite of asking several times never really understood what position he held, in Kibaha to prove that we were actual students. This important man, in his turn, needed an introduction letter from Mr. Masika, director of studies at DIT. Went to Mr. Masika and asked for the introduction letter. He, Mr. Kusekwa and Mr. Ng’ombo discussed for a while and then Mr. Ng’ombo tried to explain to us the outcome of the conversation. We got confused. 4/4 Monday Who is going to write the letter? Is anyone going to write the letter? Better go to DIT to push a little. They said it was to be written that day. 5/4 Tuesday Public Holliday. No letter. 6/4 Wednesday Determined that this was the day that we were to get the letter, we went straight to the top, Mr. Masika. The letter was almost finished. We put a final touch to it, but a reference number was still missing. One of the secretaries went to fetch it. She came back empty handed. She left again. Came back empty handed. She left again. She returned with some documents. A few minutes later we had the precious letter in our hands. We went to see Kusekwa for transport to Kibaha, but he told us we could not go because he had not gotten any reply from someone, we never understood from whom. On our way out we met Mr. Ng’ombo who saw the letters and said: Let’s go! So we went. The important person in Kibaha, who was supposed to issue our research permit was not there. We left the letter to collect the answer another day. 7/4 Thursday Mikael goes to Masasi and I, Christina, continues the chase for the permit on my own. At DIT people busy with meetings. No permit. 8/4 Friday Going to collect the permit. Waited for Mr. Ng’ombo for an hour. Waited for the car another two hours. Then the driver was gone. Waited for him an hour. Then the car had to be unloaded. Then the car needed a check-up. Then the driver had to buy water and then he needed to make a phone call. Went to Kibaha. The important person was at the mosque praying. Waited for two and a half hours. After some more pushing from Mr. Ng’ombo we finally got the permit. Too late to go to Bagamoyo, better wait till Monday. 11/4 Monday Went to Mr. Ng’ombo’s office. He was not there. Called Sithole, who asked me to come down to IPS office to make plans for the afternoon. Went there. Made plans for the afternoon. Sithole asked me to wait when he called a guy to make arrangements for the car. The guy was in a meeting. Sithole asked me to wait a couple of minutes. After an hour I asked Sithole to call me when he got hold of the guy. On my way back to DIT I met John, the driver, and he asked if I was ready to go. Confused. I was ready, but was Mr. Ng’ombo? And who had arranged for transport? Was there really a car? Just like that? Hurried to Mr. Ng’ombo’s office and he was there. Asked if we could go to Bagamoyo. Mr. Ng’ombo said that we could, if we hurried up. I asked if I should tell

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John that we were on our way, but he told me there was no need for that. After that he spent half an hour filling in a form (which has to be filled in if you work outside of Dar). Then he talked to a student for ten minutes. Then he and two other teachers talked about an iron that was leaking for about twenty minutes. Then, we went to the cars. John and the car had gone away. Waited for an hour. Realized we were not going to make it today. Sithole called and said we better postpone our fieldtrip till Wednesday. I went to a bar and had some drinks…. 12/4 Tuesday Waited for Mr. Ng’ombo for two hours. Went to Bagamoyo. The Regional Commissioner had gone to Kibaha for the day. Left the letter there. Had to come back the next day to get the answer. Went to a governmental institution for education in Dar es Salaam to get equivalent letters for the Dar es Salaam region as we got from the very important man in Kibaha for the Bagamoyo region. This important man was not there either. Left them and was told to come back tomorrow. 13/4 Wednesday Waited for Mr. Ng’ombo for two hours. He had picked up the letters from the Dar es Salaam region. These we had to hand out at three different locations in Dar es Salaam. Went to the cars. We could not have a car to go to Bagamoyo, but were allowed to hand out the three letters. Did so, and was told to come back tomorrow to get the answers. 14/4 Thursday Mikael continue the chase. Went to Bagamoyo. Regional commissioner still not there. His secretary told us to wait for his substitute who was in a meeting. Left to spend the time at a nearby museum. Back at the commissioner’s office a young man, the substitute, came to see us. He was very helpful and within an hour we could finally start to work. But it was getting late. Had to come back another day. In this manner it went on and on and on….. until 19/4 when we finally got another field visit with DIT. This field visit went to a village called Mlingotini – the name as beautiful as the village itself. The story of Mlingotini is told further down. Then we continued struggling with the transport, guide and permits until: 3/5 Tuesday Waited for Mr. Ng’ombo and the car and the driver in the usual manner. Went to the village where our permit was valid. The Chairman told us we could not do any interviews this day. They need to be prepared. Had to come back another day. 4/5 Wednesday Waited for Mr. Ng’ombo and the car and the driver as usual. Went to the village. Or so we thought. When arriving the village turned out to be a Governmental institution, where we could not do any interviews. After all this time and petrol spent on waiting and driving around to different governmental institutions we had received a permit for a place of absolutely no interest for us. I could not hold back the tears. I hated Tanzania.

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Figure 3.5. The beautiful village Mlingotini

3.4.7. Mkuranga

13/4 Field Visit 6 Written by Mikael As we at this time were chasing the permit with DIT without getting anywhere we began to look for other ways to collect data. We called Sithole at IPS, who agreed to take us out on a few field trips. Together we came up with a suitable village to visit and when to go. I met up with Sithole and after an unusually short wait we started our trip to the chosen site of interest, Mkuranga, situated about 25 km south of Dar es Salaam. Since we were very busy at this time (waiting for cars and trying to meet with important persons who are not present) only one of us, Mikael, went on this field visit. I was amazed how smoothly everything went with a private company instead of DIT. There was never any talking to the Chairman and no delays, we just started working. At first I was about to ask if we should not just shortly introduce ourselves to the Chairman before continuing. Having heard stories about how important this introduction was, but also having learnt how time consuming it could be, I hesitated but decided to not mention it. Mkuranga was a village with a rural feeling although it was still quite close to Dar es Salaam. Along the main road there were many small businesses and we got a good set of observations. Apart from our main goal Mkuranga we also stopped in some other villages on the way back including Viunzi, Kigamboni and Mwanambaya. Since the field trip had gone so well I asked for another field visit with Shella Beach the coming week, which they agreed to.

3.4.8. Mlingotini

19/4 Field Visit 7 Written by Christina and Mikael Our seventh field visit went to Mlingotini, a small and unbelievable beautiful village without much commerce, which was what we had been looking for. It was situated by the sea and offered plenty of gorgeous views and a very peaceful atmosphere. Since it was Tuesday, we suspected that Mr. Ng’ombo had classes in the afternoon but he never said anything and we stayed for a full day. Our local guide, appointed by the Chairman, was very helpful and we got many observations. To show our gratitude and since we had not eaten anything during the whole day we offered to take Mr. Ng’ombo and our ever smiling driver John to dinner. During the meal we understood that Mr. Ng’ombo actually was supposed to have had classes that afternoon but skipped it for our sake. Even though we were many times exhausted by frustration of what we felt was inefficiency and sometimes pure silliness, we are aware that money is scarce and our visit was a strain on DIT’s resources. We are therefore much grateful for all what they actually did do for us.

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Figure 3.6. Driving off road in Sadaani Game Reserve

3.4.9. Miono

20/4 Field Visit 8 Written by Christina and Mikael At ten o’clock after the usual two hours of waiting we, Sithole and the driver Richard left Shella Beach office for Miono, more than two hours drive away. As the last field visit with Sithole went very well our expectations today were high. We felt a bit worried though when he mentioned that we were also to make a second try to reach the salt factory in Sadaani Game Reserve. Getting stuck in the mud on our first try was still a very vivid memory. Five weeks earlier on our way to Sadaani Game Reserve, our very first field visit, we had passed Miono and since then always wanted to return as it seemed like a suitable village to investigate. We reached Miono without any problems. It was the weekly market day and the streets were alive with people. After visiting and interviewing a couple of businesses we called it a day as we knew that Sithole wanted us to continue to the salt factory. The road to Miono had been very good, but turning right onto the deserted road to the salt factory, we instantly realized that this was not true for the remaining distance. Already after few meters there was a big pool of mud in front of us. Fortunately this one was possible to cross, but as the next one appeared already after a few more meters we seriously hesitated on continuing. But Richard, our young and apparently adventurous driver, did not share our anxiety. Our hope grew when we without trouble passed the spot where we last got stuck, but the road ahead did not seem to be in better shape than last time. And after a little while we were driving more off the road than on it, at a very, very slow pace. Considering the late hour, only two hours till dusk, we advised our companions to turn back and eventually they agreed. It is not only skills that take you through a mud pool you also need a little bit of fortune. As we changed direction apparently so did our luck. We got badly stuck in a sea of mud. After two hours of struggling with the car not moving an inch with animals approaching (lions?), lack of drinking water and the arrival of darkness we decided to leave the car behind and walk back to Sadaani village. We had earlier seen a sign for a safari lodge and here we hoped to find some help, preferably in the form of a huge monster truck. The walk to the lodge was exhilarating. We had to wade through a small river and were constantly on the look-out for wild animals. The lodge at first seemed deserted, but after a while we found some people there. Unfortunately they could not help us as there is a particular office in Sadaani village, Tanapa (Tanzania National Park), responsible for taking care of these matters. However, they were very friendly and kindly offered to take us there. The walk to the Tanapa office was equally exciting, following the Sadaani coast line, well-known for its beautiful beaches. And once more crossing the river, this time the moon alone illuminating our path. Arriving at Sadaani we were asked to sit down at the local outdoor pub while waiting for a car to be arranged. For the first time we were out in a village when it was dark and this was an adventure in itself. During the day people are moving slow and try to be in the shade as much as possible. The men are often away, working on the fields, leaving the village empty. But this evening the tiny main street was bustling with people running errands, seeing to

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domestic affairs and maybe even have some time for socializing. In every shop there was a kerosene lamp burning (Sadaani is not electrified) lifting the dome of darkness that was closing in on us. An hour or two later the monster truck appeared, in form of the safari lodge’s huge truck used for taking visitors around the game reserve. So it was the safari lodge that helped us in the end, it was just the usual round of bureaucracy that needed to be over and done with. But we were tremendously grateful. Shortly thereafter our car was released from the mud and we were very happy. But soon there was another threatening cloud; our driver did not want to go home in the night, which was understandable as it was pitch dark and the roads are difficult enough to drive on in daylight, but we had a field visit planned with DIT the following morning and we really needed all field visits we could get to gather information. But we decided to stay and were accommodated at the safari lodge for the night. As it was low season (rain season) the lodge was actually closed and the owner was away. The wonderfully kind staff, who looked after the place during low season, unlocked one of the lodges that was situated right on the beach and there we stayed the night, free of charge. The next morning we got up with the sun and before heading off we gave the staff a big tip. As we still saw a chance to make it for the field visit with DIT we asked Richard to step on the gas, but as we were flying along the road not only fearing for our own lives but for the people’s and animals’ along it we deeply regretted voicing our urge for a hasty return. Reaching Dar es Salaam we went straight to DIT for the prior agreed upon field visit. But Mr. Ng’ombo was not there and when he arrived he told us he was not able to go this day. Having had enough adventures for the time being we quite happily went home to the very safe environment of our hostel.

3.4.10. Mbweni and Kibaha Pi Ya Dege

18/5 Field Visit 9 Written by Christina Our field visits with IPS had always been better, than the ones with DIT, in terms of efficiency and early departures, but this day they by far surpassed themselves. We had agreed on meeting up at 8.30 and at 8.32 Sithole called me and asked where I was. I had just arrived and was standing outside the door so two minutes later we were on our way towards Tegeta, a peri-urban village half an hours drive north of Dar es Salaam. Reaching Tegeta we continued on the usual bumpy dust roads carrying us into more rural areas. We stopped in Mbweni, a village situated near the cost, and went around to the different businesses and institutions and asked our questions. People were much accommodating so we quickly gathered the obtainable information. We were particularly interested in bigger loads, e.g. welds and engines, and to measure the current on these, but unfortunately there was a power cut at the moment. Next we headed for Kibaha and vicinity. Without encountering any problems we proceeded with our inquiries. After a very hot, long day and with a great number of observation and a notably empty stomach, but disappointingly without any measurements we decided to go home. But as we headed out onto the big road I saw a man welding and as my desire for measurements far exceeded that of food I found myself shouting: “- stop the car, I need to measure on that weld!”. I took the measurements and then we could continue home, me beaming in the back seat hugging my notepad with the very important figures on the weld.

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Figure 3.7. Rain season in Tanzania

3.4.11. Kimbiji and Mjimwema

26/5 Field Visit 10 Written by Mikael We had chosen the village of Kimbiji for our next field trip. To reach it you have to cross the harbour of Dar es Salaam by a pontoon boat. Once at the other side you immediately get a rural feeling of being far from the city even though the harbour is only about 100 m wide. The road continues south along the cost of the Indian Ocean and the most distant village on this road, about 45 km from Dar es Salaam, is Kimbiji. As usually the road was rough and today we were unfortunate to get a less modern vehicle than we had had before. It had no seat belts, the back seat was loose, some doors couldn’t close properly and it made very much noise jumping up and down the road. The latest weeks the rains had been very heavy and some of the road hade been washed away down into the river. The driver did not seem to hesitate crossing rivers were only half of the road remained. Finally at Kimbiji we saw the Chairman and handed over one of the research permits that we had spent so much time on getting. Unfortunately Kimbiji was not electrified so I only a few observations were made. Soon we decided to go back, stopping in Mjimwema on the way, a village that I found more proper for our investigation. When we came back to the car it was crowded with people. Some villagers had asked us for a lift. Parts of the backseat came totally lose from the pressure of the ones sitting behind it. Nobody seemed to notice. In Mjimwema we handed over our permit and could start to work without any problems.

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Figure 3.8. Man struggling with his bike up the hill

3.4.12. Kisarawe

27/5 Field Visit 11 Written by Mikael On the map Kisarawe seems to be situated just a fem kilometres west of Dar es Salaam airport. An easy and smooth ride for once I thought. But what our map did not show was the topography. The landscape become increasingly hilly something I had not expected. The scenery was beautiful and I enjoyed the ride very much. Hills turned into small mountains and the paved road came to an end, as it had done so many times before. The travelling villagers we saw on the road had a hard time climbing the hills with their heavily loaded bicycles and trolleys. Kisarawe was a silent village on top of a hill. It had a small bus square in the middle and there we started to do some interviews. Some households in Kisarawe, situated on the top of a hill, had a splendid view over the surrounding flat landscape. On the way back in the car I even saw Dar es Salaam’s skyline in the horizon with only a few tall buildings.

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4. Analysis

4.1. Aggregation of Stochastic Loads As explained in chapter 2.3. the business concept of IPS, for whom we are developing the computer program, is to deliver electricity to rural villages in Tanzania as cheap as possible by using low cost transmission equipment. For this they need a tool that estimates the electrical load more exact than the ones in use at present, which overestimates the load considerably. For example Tanesco, the national utility in Tanzania, does not go into further detail on how each load behaves and thus the aggregation5 of the loads is not satisfactory [13]. Many loads are used on and off during the day and the probability that all loads are on at the same time is low. To analyze this situation we have used Monte Carlo simulations, which is a type of simulation that uses random numbers to imitate a stochastic variable. Let us use an iron used in a household as an example. In a village of 1000 households it is reasonable to assume that there are roughly 400 irons. From our collected data we know that they are used on the average once a week for 39 minutes, the power is 1000 W and we assume they are only used during day time, between 6 am and 8 pm. Hence, 400/7 = 60 irons would be used on a normal day. If we just multiply the power of one iron with number of irons we would get 60 kW. But the Monte Carlo simulations, figure 4.1., indicate that not more than 6 iron would be on at the same time, that is a maximum load of 6 kW. Note that this is an extreme example as the iron is an appliance used relatively seldom.

Figure 4.1. Simulation of 60 irons over a day

5 Aggregate means to combine into a single group or total

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Even if we make 1000 simulations and pick out the maximum value for each hour, figure 4.2., the total load will not be more than 12 kW. The 1000 simulation could represent 1000 possible outcomes or if you so want 1000 days.

Figure 4.2. The maximum power from 1000 simulations of 60 irons over a day

4.1.1. Stochastic and Deterministic Loads

Loads can be categorised after how they appear over a certain time period. Deterministic loads are known to their power as well as at what times they are on. For example, the sun sets and rises in Tanzania at the same time year round, hence a security lamp, which is on when it is dark is categorised as deterministic. Stochastic loads are only known to their power. However, in our case, having gathered extensive information about the loads, we do have some knowledge of when the load is in use and still call it stochastic. We also have our own terminology, explained here with the stochastic load mill an example. We conducted many interviews with villagers owning or operating milling machines. We have asked when they open and when they close (opening hours). Hence we know when the mill is definitely NOT on. We discovered that they, on the average, run the milling machine about four times per day for about one hour. As mentioned before, we call this “number of continuous sequences per day” and “length of one continuous sequence”. But we do not know at what time during opening hours these sequences occur, hence the load is stochastic. Deterministic loads are easy to summarize; it is just to multiply the number of loads with the power in the usual manner. But how do we aggregate stochastic loads? This is a central question in our Master Thesis and in this chapter we will describe the line of working process that ended up in what we call probability theory.

4.1.2. Geometric Addition

For the aggregation of the stochastic loads we started with an equation that Mr. Bergman, the initiator of IPS, wanted us to examine:

......32

322

212

1 NPNPNPPtot ∗+∗+∗= (1)

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Ptot is the total aggregated load Px is the power of one load, e.g. an iron Nx is the number of irons

We could also try different variations like:

....32

322

212

1 +∗+∗+∗= NPNPNPPtot (2) The idea is that when aggregating stochastic loads these equations would give a better approximation of the actual total load than just adding the power as done in some load estimating models, that is:

332211 NPNPNPPtot ∗+∗+∗= (3)

The line of action was: from our many observations we calculated the average power, opening hours, length of one continuous sequence and number of continuous sequences per day for each load. Using Matlab we wrote scripts that performed Monte Carlo simulations. In these scripts we defined the loads with the calculated average values. The script takes the number of each load as input arguments and produces a graph of the total power. Either you could use one simulation, as in figure 4.1. or you can make many simulations and calculate the average for each hour. In figure 4.3. we have made 1000 simulations of 60 irons. The uppermost curve is the maximum value of the simulations, just like in figure 4.2. The dotted curve is one simulation, like in figure 4.1. and the more even curve is the average of the 1000 simulations.

Figure 4.3. 1000 simulations of 60 irons over a day

Now we could compare equation (1) or (2) with the curves. In the case of only one load the equations will be the same:

7.760*1000 21

21 ==∗= NPPtot kW (4)

In figure 4.4. we have introduced the equation in the same graph as the maximum and average power curves. The uppermost curve is still the maximum power through the 1000 simulations. The lower and thicker curve is the average and straight line is the

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equation. We see that the equation agrees very well with the Monte Carlo simulations. It is much closer to the simulated values than equation (3) that would calculate a value of 60 kW.

Figure 4.4. 1000 simulations of 60 irons over a day and equation (1)

But what happens if we change the number of irons? Figure 4.5. to 4.7. represent 10, 500 and 1000 irons respectively (in figure 4.7. we only made 500 simulations as the laptop opposed to working that hard on a Saturday – the result is as good)

Figure 4.5. 1000 simulations of 10 irons over a day and equation (1)

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Figure 4.6. 1000 simulations of 500 irons over a day and equation (1)

Figure 4.7. 500 simulations of 1000 irons over a day and equation (1)

We clearly see a tendency for the curve generated with equation (1) to sink compared to the Monte Carlo simulations when the number of irons is increased. This is logical as the Monte Carlo simulated curves are proportional to the number of irons, while equation (1) is only proportional to the square root of the number of irons, thus the Monte Carlo simulations increase more than equation (1) when the number of irons is increased. This means that equation (1) is only valid within a certain range of number of loads and the range is different for each load. To deal with this situation we introduced constants in equation (1) and (2):

......*** 32

322

212

1 NPNPNPPtot ∗+∗+∗= λβα (5)

....*** 32

322

212

1 +∗+∗+∗= NPNPNPPtot λβα (6)

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By changing these constants we could control the position of the line generated with equation (5) or (6). The idea was to adjust the constant according to the Monte Carlo simulations, either according to the average power curve that would give a correct quantity of energy over a longer period of time (the area beneath the curve and line would be the same) or according to the maximum power curve that would give the maximum power, which the cables could be dimensioned for. To make the equations agree with the Monte Carlo simulations for any number of loads we made them dependent of the number of loads and wanted to test different functions. We started with basic linear proportionality:

111 * mNk +=α 222 * mNk +=β 333 * mNk +=λ ……

We determined k and m for different loads and inserted them into equation (6). For the loads iron (α) and weld (β) we got these constants:

0*0023.0 1 += Nα 0*017.0 2 += Nβ

With these constants equation (6) “followed” the Monte Carlo simulations just like we wanted. Picture 4.8. to 4.10. are three examples.

Figure 4.8. Monte Carlo simulations and equation (6) of 30 irons and 3 welds

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Figure 4.9. Monte Carlo simulations and equation (6) of 60 irons and 5 welds

Figure 4.10. Monte Carlo simulations and equation (6) of 400 irons and 20 welds

But then we realized that if we inserted the equations of the constants into equation (6) we would not get just the power squared under the square root, but also the number of loads. And with that the square root would disappear!

2211

22

2212

11

22

212

1

*017.0**0023.0

**017.0**0023.0

**

NPPN

NPNNPN

NPNPPtot

∗+=

=∗+∗=

=∗+∗= βα

Which is equation (3) with constants. We had just made a long detour. At this point of time we had also started to consider probability theory. Why taking this detour with constants when there exists a “finished” theory for stochastic variables?

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4.1.3. Probability Theory

We started experimenting with our Monte Carlo scripts. Let us say that we have 100 irons that are used for 39 minutes once per week between 6 am and 8 pm. The average of 1000 simulations produces the graph in figure 4.11.

Figure 4.11. Monte Carlo simulations of 100 irons The probability for an iron to be on is (39/60) hours per (14 hours*7 days), that is 0.0066. If this number is multiplied with the power, 1 kW*100 irons we get 0.66 kW. This seems to agree very well with the Monte Carlo simulations. Encouraged by this we contacted Mr. Tobias Rydén, professor at the Centre for Mathematical Sciences, Lund Institute of Technology, who verified that probability theory for random variables can be applied in our case as long as the stochastic variables are uncorrelated. Uncorrelated means, non-dependant, if one person in a household is ironing that should not mean that the probability for the neighbour to be ironing is higher. The theory: A stochastic appliance with two power levels, P1 and P2. In terms of probability theory the power of the appliance is a stochastic variable, X. At specific moment, for example at 10 am, the power of the appliance could be either P = P1 or P = P2. a is the probability that the power of the appliance is P1. The probability for the power of the appliance to be P2 is then 1-a.

The expectation, E, is: E[X] = a * P1 + (1-a) * P2 Expectations can be linearly added: E[X+Y] = E[X] + E[Y] E[n*X] =n* E[X] All different loads like mills, lights, fridges etc. can then be added in the same way according to above. To get information about how the power outtake changes we can calculate the variance, V.

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V[X] = E[X2] - E[X] 2 = a * P12 + (1-a) * P2

2 – {a * P1 + (1-a) * P2}2 The variance can also be added linearly: V[X+Y] = V[X] + V[Y] V[n*X] = n*V[X] The standard deviation, D, is: D [X] = [ ]XV The Central Limit Theorem says that a sum of unrelated stochastic variables with arbitrary probability distribution is approximately normally distributed as long as the number of components in the sum is large enough. With this approximation and with the expectations and the variances known we can present additional information about the maximum power outtakes using the function Φ (x). Let us call the stochastic variable that is the total aggregated power from a village X. Φ (x) is then the probability that X is smaller than or equal to a chosen power, x, if X is a normally distributed with the expectation 0 and standard deviation 1. This could also be written as: Φ(x) = P(X ≤ x) if X Є N(0,1).

When the expectation and standard deviation are not 0 and 1 (as in our case) instead we must use: Φ((x – E)/ D) = P(X ≤ x) if X Є N(0,1). Certain values of x that are commonly used are called quantiles. For example, the quantile 0.05 would give you the total aggregated power, to the village of interest, that with the probability of 95 % not will be exceeded.

4.1.4. Conclusion

Both these methods could be used, but probability theory has one major advantage. With the extensive information we have gathered during our field visits we are able to calculate the probability for each load for each hour. With geometrical addition we would get one straight line for the whole day. This in addition to probability theory being the generally accepted theory for stochastic processes is why we decided to use probability theory for our computer program. Another method of aggregating loads, which is commonly used when planning energy distribution, is to use coincidence factors. However, coincidence factors and probability theory are basically the same theory.

4.2. Loads One of the key features in our computer program is the table called “Loads”. Here we have modelled different loads, with respect to power and user times, that can be found in Tanzania. To easier distinguish between our modelled loads and loads in general or a specific observed machine or appliance we have used italics. The required information

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was collected through the more than 100 interviews that we conducted with households and businesses in rural and peri-urban areas. All these interviews are to be found in their original form in chronological order (after which they were conducted) in appendix A. The observations were then rearranged after what kind of load they represent and what kind of customer type they were derived from in order to get a lucid overview and an easy-to-work-with presentation of all the data. This representation together with detailed information on all assumptions and calculations are located in appendix C and D. In this chapter we describe every load by its power and probability vector. Our purpose is to give the reader a general idea of the characteristics of the load. The probability theory is explained in chapter 4.1.3. and exact calculations are found in appendix C. The power of each load has been calculated as an average of the observed loads. If there are observations that differ greatly from the others or if we have very few observations of a load we have modified it to better agree with expected result. Many loads, especially household appliances and lights, can be used by different customer types and for each customer type they display an individual usage pattern. If these usage patterns are very different we have subdivided the loads according to these patterns. For example, the radio is used by both households and shops. Households tend to use it only a couple of hours during the day, but often the whole evening whereas shops have it on all day non-stop, but turn it off earlier in the evening. This results in two unique probability vectors, Radio Household is represented by low probabilities during daytime that increases in the afternoon and peaks at 7 pm, while Radio Business has a high probability to be on during the day, it even reaches 1 in the afternoon, but begins to decrease already at 4 pm. To determine the probability vectors we have used two different methods. When applying the standard method we have looked at every hour of the day individually. For every hour we have simply counted how many of the interviewed people that have stated that the load is on that hour divided with the total number of that load. The resulting fraction is the probability. For example, the probability for Radio Business is 0.97 at 5 pm. This means that if you randomly pick out a radio in a shop at 5 pm in rural Tanzania this will be on with a probability of 0.97. An alternative interpretation is that 97 % of the radios will be switched on at this time. If nothing else is stated this is the method we have used to generate the probability vectors. This procedure is appropriate when we have many interviews. If the same method is applied to loads with few observations the resulting probability vector would have variations during the day that is true for those specific loads we have examined, but might not give a correct picture on how the load is used in general. In these cases we have instead looked at the load over a longer period of time, usually the opening hours6. We have counted how many hours the load is on during the time period and divided by the total number of hours for the same period. The resulting probability is then assigned to all hours, which will give rise to an even distributed probability, hence we have given this method the name “even distribution method”. A small example:

6 Opening hours is here used in a broad sense, meaning the hours when the load has probabilities ≠ 0.

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Table 4.1. Example of the “even distribution method”

Appliance Hour 1 Hour 2 Hour 3 Hour 4 Appliance 1 on on off off Appliance 2 on off off off

Appliance Probability

hour 1 Probability hour 2

Probability hour 3

Probability hour 4

Appliance 1 0.50 0.50 0.50 0.50 Appliance 2 0.25 0.25 0.25 0.25 Average appliance

0.375 0.375 0.375 0.375

This technique is also used for loads that are used randomly over a period of time, e.g. an iron that is used 30 minutes every week, but no specific day or time of day is stated. In some cases where we have few observations we have also used Monte Carlo simulations to adjust the probability vector. Some customer types have loads with unique usage patterns and consequently ought to have their own specific probability vector. But if they are very rare we have instead assigned them the most suitable of the already defined loads. Another dilemma we encountered was how to handle the situation when different customer types “share” a load. They might display close enough usage patterns to constitute only one load, but still differ slightly. In this case the number of interviews from each customer type will affect the outcome. Let us look at the Radio Business again: Three different customer types have been considered when designing this load: 4 shops, 5 bar/restaurants and 6 hairdressers. If we used these observations without any modification the customer type hairdresser will have the strongest affect on the resulting probabilities. But in reality this is not their normal frequency of appearance in a rural Tanzanian village. From our field knowledge we estimate that for every bar/restaurant there are 2 hairdressers and 10 shops, hence the customer type shop should carry the greatest weight. To solve this problem we have introduced “weight factors” and the value of these is simply estimated to the best of our ability. In order to model our loads after as much data as possible and with that getting a more accurate result we have used material from two other surveys from Sub-Sahara Africa. The first one, which has concentrated on interviewing businesses, is Method for Rural Load Estimations, 2004, by Henrik Blennow [13] and the second one, concentrating on households, is Characterization of power system loads in rural Uganda, 2002, by Frances Sprei [14]. The interviews done by Blennow [13] in Tanzania have been put into appendix B and are of the same structure as the appendix with our own interviews. His observations have then been rearranged and put into the easy-to-work-with presentation in appendix D where they are mixed with our own observations. The interviews of households conducted by Frances [14] in Uganda have been used, where feasible, to verify our own results obtained in Tanzania.

4.2.1. Security Light

Since security lights are turned on the whole night energy saving tubes are of the used. The power we have decided on and that is an average of all observed security lights is 35 W. As the name indicates a security light is used for security reasons. It is a very common load both for households and businesses. They are put up on the exterior of the house and

40

are supposed to scare off burglars as well as supernatural beings. Security Light is the most heterogonous load we have encountered, in regard to user times, as it depends on when the sun rises and sets. Tanzania is situated very close to the equator, thus sunrise and sunset take place at almost the same time all year round. We get this probability vector:

Table 4.2. Probability for Security Light

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 1 1 1 1 1 0.95 0.36 0 0 0 0 0 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0 0 0 0 0 0 0.57 1 1 1 1 1

4.2.2. Lights

Ordinary lights have a higher average power than security lights since they are more usually bulbs and more seldom tubes. The power we have assigned is 44 W. Households tend to use light sometimes in the early morning hours and in the evening. Shops and hairdressers indicate one pattern and bar/restaurants have a slightly different pattern with higher probabilities for later hours. Consequently we have separated lights into Light Household, Light Business and Light Late Business. We also realized that a Light Day was required. Other customer types, which have not been included in the calculations, can be assigned one or more of the four defined loads (for example mosques display the same pattern as households). Frances [14] has more observations on Light Household than we have. A probability vector derived from her interviews gives a very similar picture to the one we have obtained. Our vector can bee seen in table 4.3.

Table 4.3. Probability for Light Household

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0.04 0.080 0.011 0 0 0 0 0 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0 0 0 0 0 0 0.45 1 1 0.95 0.43 0.081

Light Day is not based on any particular interview. When we created our customer types we simply needed a light that was on during the day, see table 4.4. As we do not want overlapping when assigning both a Security Light and a Light Day to a customer type it is derived from the opposite of the Security Light.

Table 4.4. Probability for Light Day

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0.050 0.64 1 1 1 1 1 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 1 1 1 1 1 1 0.43 0 0 0 0 0

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Light Business includes shops and hairdressers. Some of our interviewed businesses have stated that they do not put on the light until 8 pm and some closes at 7 pm, therefore the probability never reaches 1 as it does for households. This can be seen in table 4.5.

Table 4.5. Probability for Light Business

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0 0 0 0 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0 0 0 0 0 0.13 0.47 0.8 0.67 0.53 0.13 0.067

Light Late Business is based solely on the customer type Bar/Restaurant. It is built on relatively few observations which resulted in a somewhat discontinuous vector. The vector obtained from the standard method has been adjusted slightly according to what Monte Carlo simulations suggest. The final result can be seen in table 4.6.

Table 4.6. Probability for Light Late Business

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0 0 0 0 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0 0 0 0 0 0 0.35 0.90 0.90 0.70 0.66 0.42

4.2.3. Radio

Radios encountered in rural areas are often very small and of a power of about 10 W. Only few are equipped with tape recorders with higher power. We have decided on assigning our general radio 12 W. When analysing whether different customer types have different usage patterns we came to the conclusion that households show one pattern; mainly evening use but even sometimes during day and morning. Shops, bars/restaurants and all other customer types show another pattern; non stop use during working hours. Bars have a higher probability to have the radio on at later hours than shops and hairdressers and could constitute a load of their own, but since radio has such a low power the importance to separate is smaller and we came to the conclusion that the load radio should be divided into Radio Household and Radio Business. The vector for Radio Household, table 4.7., shows a realistic picture with a peak in the morning at 7 am and another peak at 7 pm, which is quite steep. We observed a small peak at 2 pm for which we do not see any reason. Therefore we have adjusted this figure to the mean value of its neighbours.

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Table 4.7. Probability for Radio Household

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0.048 0.36 0.41 0.36 0.34 0.24 0.19 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.19 0.19 0.24 0.29 0.34 0.39 0.40 0.69 0.62 0.59 0.36 0.17

For Radio Business, table 4.8., that includes shops, bars/restaurants and hairdressers we also needed to use “weight factors” of 10, 1 and 2 respectively.

Table 4.8. Probability for Radio Business

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0.22 0.67 0.77 0.79 0.98 1 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 1 1 1 1 0.97 0.97 0.97 0.95 0.88 0.66 0.060 0.050

4.2.4. TV

As one could expect our observations for TV predominantly come from interviews with households. We also have two interviews with restaurants and one with a guesthouse that gave us user times for a TV. These customer types report the same pattern; mostly used at evenings but also sometimes during day. The power of the TV will be 60 W. Unlike evening use of TV, which is heterogonous and causes no problem, day time use is more difficult to handle. About 25 per cent of the interviewed households stated that they sometimes watched TV during the day, which we somehow have to account for, but the information is not enough to give a realistic probability vector. If we just use the standard method we would get a vector with randomly dispersed peaks during the day. Instead we apply the even distribution method and get a very low and even probability vector for the day hours (6 am – 6 pm) and then use the standard method for the evening hours (see table 4.9.).

Table 4.9. Probability for TV

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0.060 0.060 0.060 0.060 0.060 0.060 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.060 0.060 0.060 0.060 0.060 0.060 0.31 0.57 0.73 0.74 0.50 0.19

In addition to the information about the TV in households, we are also aware of another TV load, TV Video Show, table 4.10. A video show is a place where the villagers can come and see TV broadcasts or recorded material. This load is designed after two observations. Blennow [13] have come to the conclusion that a TV in a Video Show runs from 10 am to 11 pm. The observation we have is from 2 pm - 11 pm. Hence we have decided on the following vector:

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Table 4.10. Probability for TV Video Show

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0 0 0.50 0.50 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.50 0.50 1 1 1 1 1 1 1 1 1 0

4.2.5. Fan

Running on medium speed the average power is 55 W for a ceiling fan. This load will be divided into Fan Household and Fan Business. Households use the fan mostly at night, while businesses use it randomly during working hours. Fan Household, table 4.11., is calculated from households only, but other customer types like Health Centre and Guesthouse can also be assigned this load. In addition to the most common use, at night, some households also have the fan on during the day and in the evening. If we look at Frances [14] observations of household fans they are all ‘during night’ with no day time use reported. All her observations are from a cooler area than those we have visited, which might explain why no day time use is reported. In the case of fan, a load which is dependant on the climate, you have to consider from what region the observations are obtained. In our case the probability vector is valid for the hot and humid Dar es Salaam area during summer. Almost all interviewed household report low use in winter or summer use only, but when we made a rough calculation we found the difference negligible.

Table 4.11. Probability for Fan Household

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0.68 0.68 0.68 0.68 0.59 0.59 0.045 0.045 0.045 0.045 0.045 0.045 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.23 0.32 0.59 0.77

From our six observations on Fan Business (five hairdressers and one shop) we calculated the probability vector that can be seen in table 4.12. This vector we believe agrees very well with reality. Some few businesses have the fan on the whole day while most of them have it on only when it’s really hot, in early afternoon. The probability never reaches one because hairdressers often turn the fan on when they have a customer, but then turn it off as soon as the customer leaves, to save electricity and with that money. We did not need to use weight factors in this case as we consider the vector correct for both hairdressers and shops. Possibly, is the probability for a fan in a shop to be on in early afternoon a little bit higher, but this is too small a difference to take into consideration.

Table 4.12. Probability for Fan Business

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.18 0.38 0.40 0.42 0.42 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.42 0.59 0.59 0.51 0.38 0.38 0.38 0.22 0.17 0.17 0 0

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4.2.6. Iron

This load, with the general power 1000 W, is almost only used by households and tailors. They have much the same usage pattern in the sense that the iron is used by both customer types stochastically and therefore will have probability vectors with an even distribution. However, households iron about 30 minutes per week while tailors iron more than 3 hours per day. Thus the probabilities for Iron Tailor will be much higher. We have also taken into consideration that there is a thermostat in the iron and therefore it only consumes electricity about 50 % of the time. We assume that households do not iron when it is dark or extremely early in the morning, i.e. they iron between 6 am and 8 pm. The probability vector becomes:

Table 4.13. Probability for Iron Household

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0.0031 0.0031 0.0031 0.0031 0.0031 0.003112am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.0031 0.0031 0.0031 0.0031 0.0031 0.0031 0.0031 0.0031 0 0 0 0

Contrary to what one might think it is the iron and not the sewing machine that is the most common electrical appliance at the tailor’s. Even the tailors that have got electricity tend to use the old-fashioned sewing machine worked with the feet. The probability vector for Iron Tailor through the even distribution method is:

Table 4.14. Probability for Iron Tailor

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.061 0.26 0.26 0.26 0.26 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0 0 0 0

4.2.7. Fridge/Freezer

Fridges and freezers come of course in different sizes. We have decided to make one general load that is valid both as an average fridge and an average freezer. The power is 100 W. It seems like most household have their fridge/freezer on 24h a day while shops and bars usually report fewer hours of use per day. We have separated this load into Fridge/Freezer Household, using the even distribution method, table 4.15., and Fridge/Freezer Business, table 4.16. To this has to be added the stochastic appearance of the compressor in the fridge/freezer. We account for this by multiplying with 0.7, i.e. we think the compressor is on 70 % of the time.

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Table 4.15. Probability for Fridge/Freezer Household

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0.60 0. 60 0. 60 0. 60 0. 60 0. 60 0. 60 0.60 0.60 0.60 0.60 0.60 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60

For shops and bars/restaurants the use of the fridge/freezer is connected to opening hours and the heat peak of the day. Cold drinks, which is almost the only product the fridges are used for, sell best during hot hours, hence the fridges have to work just before those hours, which we see as a peak before noon. We also see a smaller peak in the evening that might originate from drinks getting warm again and needing additional cooling. Workers coming home from their jobs and serving dinner could also be an additional reason. No weight factors are used since the number of interviews from each customer type in comparison is close to the normal frequency of appearance in a rural Tanzanian.

Table 4.16. Probability for Fridge/Freezer Business

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0.27 0.27 0.27 0.27 0.27 0.27 0.38 0.58 0.64 0.64 0.53 0.53 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.47 0.47 0.47 0.47 0.44 0.44 0.38 0.41 0.41 0.41 0.35 0.35

4.2.8. Mill

The power of the electric engines has been investigated more closely and the results are found in chapter 4.3. They are either running with load or without load. The power of a Mill running with load will be 13.8 kW and without load 7.7 kW. The probabilities in the vectors below are the probabilities that the mill is turned on (i.e. running with load or running without load). The data collected about the probability that a mill is running with load if we know that it is turned on is limited. We have set this probability through our field knowledge to 0.50 (i.e. half of the time, when it is turned on, it is running with load and half of the time it is running without load). Milling is an important business in almost every village since agriculture is a very common occupation. Maize is usually milled but also rice and other crops can be found. There are two processes: grinding and milling. To grind is to remove the cover of the maize and is sometimes done before milling to get a flour of higher quality. Milling and grinding are considered the same when modelling the load. Though, a small part of our interviewed mills suggest that grinding differ from milling in having shorter total time of use. The average power for grinding is also a little lower than for milling. But since they often exist together and the differences are small a mean value of both will produce a correct general load, which we call Mill.

There is a big difference in total time of use per day between the interviewed mills. The highest value is about 4 times higher than the lowest. Since this is a dominating and important load there could be a reason to divide it into categories but we have come to the conclusion not to do so. One reason for this is that it would be very difficult for the person who is counting the mills to know how much the mill is used. Keep in mind that

46

this person should be able to determine what customer type, or in this case mill, he is looking at without interviewing the owner. For mills it is more difficult than for other customer types since the mills depend on the agricultural activity outside of the village and not the commercial activity within the settlement. Milling is one of few loads that display changes due to seasons. Our analyses show that the change in activity is about 200 % between low and high season. The probability vectors below in table 4.17. to table 4.19. are for Mill Low Season, Mill Intermediate Season and Mill High Season. The even distribution method is used and the average opening hour for a mill is 7 am – 7 pm.

Table 4.17. Probability for Mill Low Season

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.39 0.39 0.39 0.39 0.39 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0 0 0 0 0

Table 4.18. Probability for Mill Intermediate Season

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.58 0.58 0.58 0.58 0.58 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.58 0.58 0.58 0.58 0.58 0.58 0.58 0 0 0 0 0

Table 4.19. Probability for Mill High Season

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.78 0.78 0.78 0.78 0.78 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.78 0.78 0.78 0.78 0.78 0.78 0.78 0 0 0 0 0

4.2.9. Electric Engine

Electric engines encountered in carpentries and workshops differ greatly in both power and user times. The machines encountered are levelling machines, saws, turning lathes, drills, grinders and sharpeners. Each of these groups of machines is far from homogenous. A drill for example can be either a small hand carried tool or a large three phase stationary machine. The user times can also differ considerably within a group. Even more so than for mills and welds. Instead of creating a general Levelling Machine, a general Drill and so on we have decided to make electric engines without specifying what kind of machine they are. To create general electric engines from our observations was hard as the observations were not easily divided into heterogeneous groups and many assumptions and interpretations had to be made. We tried different methods and these are found in appendix C. Finally we came to the conclusion that four electric engines should be created. Their vectors are based on the average opening hours for carpentries, which are 7:30 am – 6 pm, and their power has been reduced according to chapter 4.3. We have set

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the probability that they are running with load, if we know that they are turned on, to 0.50 for the same reason as for Mill. Electric Engine 1, table 4.20., is a small tool with the power 380 W (running with load) and 200 W (running without load). It is used 0.2 hours per day.

Table 4.20. Probability for Electric Engine 1

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.0095 0.019 0.019 0.019 0.019 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.019 0.019 0.019 0.019 0.019 0.019 0 0 0 0 0 0

Electric Engine 2, table 4.21., is an medium machine with the power 1.75 kW (running with load) and 0.95 kW (running without load). It is used for 0.5 hours per day.

Table 4.21. Probability for Electric Engine 2

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.024 0.048 0.048 0.048 0.048 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.048 0.048 0.048 0.048 0.048 0.048 0 0 0 0 0 0

Electric Engine 3, table 4.22., is of the same power as Electric Engine 2, that is 1.75 kW (running with load) and 0.95 kW (running without load), but it is used much more, namely for 5 hours per day.

Table 4.22. Probability for Electric Engine 3

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.24 0.48 0.48 0.48 0.48 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.48 0.48 0.48 0.48 0.48 0.48 0 0 0 0 0 0

Electric Engine 4, table 4.23., is a large machine of 4 kW (running with load) and 2.2 kW (running without load). Its total time of use per day is 5.5 hours.

Table 4.23. Probability for Electric Engine 4

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.26 0.52 0.52 0.52 0.52 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.52 0.52 0.52 0.52 0.52 0.52 0 0 0 0 0 0

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4.2.10. Weld

In many villages there are welding services available in work shops. It is an important step in development to be able to make steel constructions. A weld constitutes a great load on the grid just like milling machines and some carpentry machines. The power without load (or in stand-by) has been determined to 0.85 kW. When welding the power increases to a maximum of 7.5 kW. The display to the ammeter constantly jumps between 0 and this peak value. The observations of total time of use per day is much more scattered than for mills. The difference between the highest and lowest values is about 20. This load will therefore be divided into three categories. The even distribution method is used and the average opening hours for a workshop is 8 am – 6 pm. We have set the probability that they are running with load, if we know that they are turned on, to 0.50 for the same reason as for Mill. Our observations of user times on welds are divided into three groups according to their size. Weld 1, table 4.24., is the average of the smallest group which is 0.4 hours.

Table 4.24. Probability for Weld 1

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0.040 0.040 0.040 0.040 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.040 0.040 0.040 0.040 0.040 0.040 0 0 0 0 0 0

Weld 2, table 4.25., has a total time of use per day that is 2.25 hours.

Table 4.25. Probability for Weld 2

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0.23 0.23 0.23 0.23 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.23 0.23 0.23 0.23 0.23 0.23 0 0 0 0 0 0

Weld 3, table 4.26., is used in a busy workshop and the total time of use per day is 4.8 hours.

Table 4.26. Probability for Weld 3

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0.48 0.48 0.48 0.48 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.48 0.48 0.48 0.48 0.48 0.48 0 0 0 0 0 0

4.2.11. Charger Car Battery

Even if a village is electrified far from every household is able to pay for the connection fee. Using car batteries it is possible for non-electrified households to use some small

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electrical appliances. Therefore you often find chargers for car batteries in villages and in particular in workshops. The device usually allows four batteries to be charged at the same time. The power for the total device is 65 W according to measurements and the probability vector can be found in table 4.27. We have used the opening for work shops and applied the even distribution method.

Table 4.27. Probability for Charger Car Battery

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0.83 0.83 0.83 0.83 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.83 0.83 0.83 0.83 0.83 0.83 0 0 0 0 0 0

4.2.12. Pump Filling Station

The distance between filling stations in rural areas can be far, but along the few asphalt roads they occur rather frequent. The probability vector in table 4.28. is created from timing customers refilling as well as from an interview with an owner of a filling station. Some changes have been made to make it suitable for a rural filling station. The power of the Pump Filling Station is 0.63 kW and the opening hours, 5:30 am-7 pm, from the customer type Filling Station is used.

Table 4.28. Probability for Pump Filling Station

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0.034 0.067 0.067 0.067 0.067 0.067 0.067 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.067 0.067 0.067 0.067 0.067 0.067 0.067 0 0 0 0 0

4.2.13. Razor

Razors are only used by hairdressers cutting men. They have all stated a power of 10 W, which we will use for our Razor. The average time for a cut is merely 13 minutes and there is on average five customers per day. This is why the probability for the Razor to be on is so very low, table 4.29.

Table 4.29. Probability for Razor

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.053 0.069 0.075 0.081 0.081 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.081 0.081 0.081 0.081 0.069 0.069 0.069 0.069 0.060 0.035 0 0

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4.2.14. Stand Alone Hairdryer

Stand Alone Hairdryer and Stand Alone Steamer are two devices used by hairdressers for women to set the hair into desired hairstyle. Tanzanian women do not use make up or perfume, instead they pay quite a lot of attention to their hair. During our three months visit we did not once encounter a woman (or man) with an afro. For practical or esthetical reasons they wear their hair either straightened or braided in a fancy hairstyle. The braids go from the top of the head to the hairline, from the hairline and up, from ear to ear or around the head, creativity sets the limits! False hair is also very common. We have merged the two devices as they seem to have the same usage pattern. The power of the steamer is a little lower, but this is not a problem as most hairdressers use both a hairdryer and a steamer. The average power for the two is 820 W and the probability vector, generated with the even distributed method, becomes:

Table 4.30. Probability for Stand Alone Hairdryer

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.10 0.17 0.17 0.17 0.17 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0 0 0 0

4.2.15. Amplifier/Loudspeaker

Five times a day mosques are calling for Muslims to pray. All mosques have given us similar answers of when the calling takes place but they still differ slightly. The calling usually goes on for about 20 minutes and has to be heard many streets away. We assume the power to be 100 W for the combination of an amplifier and connected loudspeakers. The probability vector in table 4.31. is obtained.

Table 4.31. Probability for Amplifier/Loudspeaker

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0.42 0.50 0.063 0.083 0 0 0 0 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0 0.42 0.063 0 0.50 0.063 0.50 0.063 0.25 0 0 0

4.2.16. Big Health Appliance

Pressure cooker, boiler, sterilizer, dryer for tools, hot plate and air-condition set are all appliances encountered in dispensaries and health centres. They have similar user times and their power is of the same size (of order 1 kW). Therefore we have congregated them to one load, Big Health Appliance with a power of 1500 W, and applied the even distribution method for its probability vector in table 4.32. The even distribution method is used with the opening hours for dispensaries and health centres, 7:30 am – 7:30 pm.

Table 4.32. Probability for Big Health Appliance

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12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.059 0.12 0.12 0.12 0.12 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.059 0 0 0 0

4.2.17. Small Health Appliance

Apart from the appliances in the load above there are many other smaller appliances (<100W) encountered at a health centre or a dispensary. These could be microscope, shake machine, sucking machine or a communication radio. They have similar user times and their power is of the same size. Therefore we have congregated them into one load. The power we have chosen for this general load is 20 W. The same opening hours as for the load Big Health Appliance and the even distribution method is used. The result is to be seen in table 4.33.

Table 4.33. Probability for Small Health Appliance

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.12 0.24 0.24 0.24 0.24 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.12 0 0 0 0

4.2.18. Video

As it is not common for households to own a video the only application for this appliance we came across was in the customer type Video Show. The power of our general Video will be 20 W. We only have one interview with an owner of a video show. Obviously it is possible for the video to be on during the same hours as the TV Video Show, but as we think they might show TV broadcasts at least half of the time we have used the probabilities from TV Video Show and divided them by two. See this is table 4.34.

Table 4.34. Probability for Video

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0 0 0 0.25 0.25 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.25 0.25 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0

4.2.19. Photocopier

The power of a photocopier is quite high during the few seconds it is copying. The power chosen is 800 W. To provide a photocopier for business purposes can pay off. There is a need for having exam papers, contracts, permits etc. copied and this is often done at a local secretary service. This customer type, together with secondary school, is the customer type that this load is modelled after. The even distribution method has been used with many

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assumptions and the resulting vector can be found in table 4.35. The average opening hours for the two customer types are 7:30 am – 8 pm.

Table 4.35. Probability for Photocopier

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.006 0.012 0.012 0.012 0.012 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0 0 0 0

4.2.20. Computer

The load computer includes both the computer and the monitor and their common power is set to 100 W. A computer can also be found at a secretary service giving possibilities for villagers to edit and print paper work, for example contracts. The load is created with the even distribution method and some assumptions for the use of the load in secretary services and secondary schools had to be made. The vector is found in table 4.36. Opening hours are as for the load Photocopier above.

Table 4.36. Probability for Computer

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.18 0.36 0.36 0.36 0.36 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0 0 0 0

4.2.21. Printer

For reasons mentioned above printers are sometimes available in rural and peri-urban areas in Tanzania. Our Printer will have the power 50 W. It is designed with the even distribution method for the same customer types as the Photocopier and Computer and with some assumptions made. Its probabilities can be seen below in table 4.37. Opening hours are as for the loads Photocopier and Computer above.

Table 4.37. Probability for Printer

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.007 0.014 0.014 0.014 0.014 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0 0 0 0

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4.2.22. Sewing Machine

Tailors are the only customer type using this appliance. We ourselves did not encounter any tailors using an electric sewing machine. Blennow [13] did though, but he has no information on user times. We have taken into account Blennow’s result in addition to measurements on our own sewing machine when deciding on power, 75 W. But for the probability vector we had to design it to the best of our abilities. We imagine that the tailor would use the sewing machine randomly during opening hours, for about as many hours as the iron. This would give the appliances the same probability vector, see table 4.38.

Table 4.38. Probability for Sewing Machine

12pm 1am 2am 3am 4am 5am 6am 7am 8am 9am 10am 11am 0 0 0 0 0 0 0 0.061 0.26 0.26 0.26 0.26 12am 1pm 2pm 3pm 4pm 5pm 6pm 7pm 8pm 9pm 10pm 11pm 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0 0 0 0

4.3. Reduced Power When modelling our loads we needed two types of information, user times and power. As Blennow [13] focused on the power and since our time and resources in Tanzania were limited, we chose to concentrate on user times when conducting interviews. In most cases we only wrote down the rated output. From the data collected by Blennow [13] and the measurements we did take we have discovered though that rated output and the measured values differ, in some cases quite a lot. Since the unique characteristic of our Load Estimation Model is that the loads are based on reality (unlike most models that assume maximum power during all hours), it is essential that we account for this discrepancy. We have therefore compared rated output and measured power on those observations that include both types of information to see if we could detect any general relation. We have also been in contact with Mr. Bengt Simonsson, senior instructor at the Department of Industrial Electrical Engineering and Automation, Lund Institute of Technology.

Below is a compilation of rated output and measurements along with a short discussion on any relation between the two. The columns to the right are measurements divided by rated output.

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Table 4.39. Measurements of milling compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:4 18.5 kW 18.3 kW (M, with load), 8.0 kW (M, without load)

99 % 43 %

H:11 18.5 kW 15.5 kW (with load), 8.5 kW (without load),

84 % 46 %

H:12 22 kW 10.7 kW (without load) 49% H:13 30 kW 31.2 kW (with load),

12.7 kW (without load) 104 % 42 %

3:11 Same mill as H:12

11.2 kW (with load), 8.5 kW (without load)

51 % 39 %

Table 4.40. Measurements of grinding compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:11 11 kW 4.4 kW (without load) 40 % H:12 22 kW 12.7 kW (with load),

10.6 kW (without load) 58 % 48 %

Average8 79 % 44 %

We see a tendency for the electrical engines to use less power than rated output when milling, on average 79 %. All these observations are taken from Blennow [13] and are all marked with (max), which we assume means the highest value registered during the process. This indicates that mills consume even less power, but since we do not have any information on exactly how much and since we do not want to reduce the power with an arbitrary amount we conclude that taking 79 % of the rated output is a fairly good description of reality. When the engine is running, but there are no grains in the machine (without load) it still seems to consume quite a lot of power. These figures are not marked with (max), which makes us believe that this actually might be the case. The machines might be old and perhaps the bearings and other parts are old and ought to be changed. It is also possible that the machine operator lacks sufficient knowledge on how to run the engine in an ideal mode. We will use 44 % of rated output as “power without load” in our computer program. Grinding9 might be “easier” work for the machine, but since we only have one measurement of this process we can not draw any definite conclusions. Hence we consider grinding and milling as the same kind of process, in regards to power, when modelling Mill.

7 Calculated from ϕcos3 ∗∗∗= IUhP , 8.0cos =ϕ . 8 Milling and grinding included. 9 Grinding is to remove the cover of the maize and is sometimes done before milling to get a flower with higher quality.

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Table 4.41. Measurements of levelling compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:5 7.5 kW 3.2 kW (with load), 2.1 kW (without load)

43% 26%

Table 4.42. Measurements of sawing compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:5 7.5 kW 2.6 kW (with load), 2.1 kW (without load)

34 % 26 %

H:14 5.5 kW 2.2 kW (with load), 2.4 kW (without load)

40 % 43 %

H:14 12 kW 10 kW (with load), 1.3 kW (without load)

83 % 11 %

Average10 50 % 27 %

Levelling machines and saws are machines worked by an electrical engine, usually smaller than the ones for mills, but still of considerable size. These machines are designed to be able to do heavy levelling or sawing, but most of the load is smaller than the maximum capacity. Therefore we consider these figures to be realistic and when designing Electrical Engine 1-4 only use 50 % of rated output as power with load and 27 % as power without load.

Table 4.43. Measurements of lathes compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:5 1.1 kW 2.6 kW (with load), 2.1 kW (without load)

235% 188%

H:14 0.75 kW 1.1 kW (with load), 0.55 kW (without load)

148% 74%

Table 4.44. Measurements of grinding/sharpening compared to rated output

Int. No. Rated output

Measurements7 With load Without load

H:14 0.5 kW 0.63 kW (without load) 126%

The measurements for lathes and grinders/sharpeners are taken from Blennow [13] and seem very much unrealistic and are disregarded. Welds differ from the other machines in the way that the power is continuously adjustable and the rated output is the maximum current the weld can produce (output) or consume (input). Therefore welds do not have to be analysed in this manner. We will just calculate the average current from all measurements.

10 Levelling included.

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4.4. Customer Types Creating customer types, in this chapter written in italics for the same reasons as in the chapter 4.2., was far easier than then creating loads. Partly because when we created loads we had the different customer types already in mind, partly because it was much more simple calculations. Our approach was to gather all interviews according to customer type in tables and calculate the mean number of electrical appliances for each customer type. In some cases we have chosen a different value than the mean. The reason for this is explained in the comments beneath the table. If we have gotten the answer “appliance used when there are people in the room” in an interview, usually indoor lights or fans in households, we have reduced the number of appliances in the tables in this chapter since they are then not used all at the same time. How much they are reduced depends on original number of appliances and how many people are living in the household. If the interviewee has told us that a load is broken or not used we have simply excluded it from the calculations. Some customer types show variation in their usage pattern over the week. E.g. Mills tend to have fewer customers on weekends and some even are closed. In appendix E you find a survey of this. The lower use of electricity during weekends is cared for in the Load Estimation Tool.

4.4.1. Households

Since households constitute the major part of the electrical load and since the amount of electricity consumed varies a lot among households, more than for most other customer types, we have decided to divide households into three categories: low load household, medium load household and high load household. The limits for the categories were at first set by Mr. Bergman, who initiated the formation of IPS, the main interested party in our computer program. However, we realized that the limits between Low Load Household and High Load Household (200 kWh/month) were set too high, since we did not encounter any household with such high electricity consumption. Thus we reduced it by 50 kWh. The limits are:

Low Load Household < 50 kWh/month Medium Load Household 50 - 150 kWh/month High Load Household > 150 kWh/month

It was easy to compute the electricity consumption over a month for each of our interviewed households and classify them according to the given figures. But then the real dilemma emerged. When the user of the program is out in the villages counting households he is expected to be able to identify the household as low, medium or high load household just by looking at it. We had to find a relation between the visual appearance of the house and the electricity consumption. The first relation we tried was if the building material of the house indicated the degree of wealth and with that the amount of electricity consumed. As concrete walls and iron roof are of better durability and more expensive than clay and thatch11 we thought this was the most obvious method. Furthermore it would be very easy for the user to apply. We grouped the households according to building material and calculated the average electricity consumption. However, the result was less then satisfactory, see table 4.45.

11 Straw or reeds used to make roofs

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Table 4.45. Mean electricity consumption of households broken up into three groups after material of the house

Clay Walls/Thatch Roof Clay Walls/Iron Roof or

Concrete Walls/Thatch Roof Concrete Walls/ Iron Roof

83.9 kWh/month 85.1 kWh/month 84.2 kWh/month

The second approach was to try the size of the house against electricity use. This turned out much better, see table 4.46. Even if the mean calculated electricity far from agree with the beforehand set up boundaries we can see that there is a tendency to use more electricity if you live in a bigger house. This is logical as you have more space to light up and there are probably more people in the household using the electrical appliances.

Table 4.46. Mean electricity consumption of households broken up into three

groups after living space

< 50 m2 50 – 60 m2 > 60 m2 68.4 kWh/month 77.8 kWh/month 112 kWh/month

It is not very easy though for the user to quickly estimate the size of the house. In the end we concluded that the best way must be to look at the general state of the house (how wealthy) in addition to estimating the size. In figure 4.12. we give four example pictures of different houses. The upper left house is most likely to be a Low Load Household. The upper right house is probably a Medium Load Household. The lower left house is a good representation of a High Load Household. The lower right house is built with the poor materials clay and thatch but the general state of it and its size could indicate that it is a Medium Load Household.

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Figure 4.12. Four example pictures of households in Tanzanian villages Low Load Household

Table 4.47. Calculations for Low Load Household

Int. no. Security

Light Light Household

Radio Household

TV Fan Household

Iron household

Fridge/Freezer Household

3:4 9 1 3:6 1 2 1 1 1 3:9 4 1 7:1 5 1 1 7:2 3 1 7:6a 4 1 2 7:7 2 1 1 7:12 3 7:14 1 2 Mean: 0.22 3.8 0.44 0.56 0.22 0.11 0.11 Chosen value:

0.22 3.8 0.44 0.56 0.22 0.11 0.11

Comments:

Light Household: (7:7) has answered: 4 bulbs used when in room, thus these are reduced to two.

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Medium Load Household

Table 4.48. Calculations for Medium Load Household

Int. no. Security

Light Light Household

Radio Household

TV Fan Household

Iron household

Fridge/Freezer Household

3:1 1 8 1 1 1 1 3:2 1 5 2 1 2 1 1 3:3 10 2 2 1 1 3:5 2 6 3 2 1 1 3:7 11 2 1 2 3:10 2 6 1 2 1 1 6:1 6 2 1 2 6:2 1 4 1 7:3 8 1 1 1 1 7:4a 5 1 1 7:9a 2 1 1 1 7:10 7 1 1 7:11 1 4 1 1 1 1 7:15 4 1 7:16 5 1 2 7:21 10 1 1 1 1 8:3a 1 2 1 1 1 Mean: 0.53 6.1 0.94 0.76 0.76 0.53 0.88 Chosen value:

0.53 6.1 0.94 0.76 0.76 0.53 0.88

Comments: Light Household: (7:9) has answered: 4 bulbs used when in room, thus these are reduced to two. High Load Household

Table 4.49. Calculations for High Load Household

Int. no. Security

Light Light Household

Radio Household

TV Fan Household

Iron Household

Fridge/Freezer Household

3:8 2 15 6 3 4 1 6:7 5 12 1 1 5 1 1 7:13 3 17 1 1 2 Mean: 3.3 14.7 2.7 1.3 3.0 0.67 1.3 Chosen value:

3.3 10.0 2.0 1.0 3.0 1.0 1.3

Comments:

Light Household: We have decided to not to use the high figure of 14.7 because we believe that a high load household does not have that many lights on at the same time,

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since there will be bulbs in rooms like closet, toilet and bedroom, in which the lights are tuned off most of the time. Radio Household: Here we have decreased the number from 2.7 to 2 because we do not believe that interviewee (3:8) is a normal High Load Household, see also TV. TV: Considering Frances’s[14] results and that observation (3:8) was an unusual house with 13 people living in it we have reduced the number of TVs. Iron Household: We fully believe that every high load households owns an iron.

4.4.2. Shop

In rural villages in Tanzania a shop is usually a part of an ordinary house in which people live. They are very small and in most cases you buy the commodities over the counter. Sometimes the shop is just a simple stand where someone is selling the tomatoes he picked the same morning. It is therefore difficult to define what a Shop is, but we have chosen to distinguish between a Shop and a Household, where the former includes all appliances used in the commercial area and the latter the appliances used in the living area, even though these boundaries not always are obvious. The calculations for Shop are:

Table 4.50. Calculations for Shop

Int. no. Security

Light Light Business

Radio Business

Fan Business

Fridge/Freezer Business

6:11 2 1 1 1 7:6b 1 2 2 7:9b 1 7:19 1 1 7:20 3 1 8:3b 1 9:3 1 1 1 2 9:4 2 1 1 9:5 1 1 1 1 H:20a 1 1 H:20b 1 1 1 1 H:20c 1 1 H:20d 1 1 1 H:20e 1 1 1 H:20f 1 1 2 H:20g 1 1 H:20h 4 1 1 H:20i 1 H:20j 1 1 Mean: 0.68 0.89 0.68 0.16 0.74 Chosen value:

0.68 0.89 0.68 0.16 0.74

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4.4.3. Bar/Restaurant

For bars and restaurants the terminology can be somewhat confusing. After some time in Tanzania we understood that what is usually meant with the word bar is not a place that sells alcoholic drinks, but a place where they serve food. It is not very common serving alcohol, but it does exist. Since we do not think there is a big difference when it comes to electricity use we have congregated the two to one customer type: Bar/Restaurant, table 4.51.

Table 4.51. Calculations for Bar/Restaurant

Int. no. Security

Light Light Late Business

Radio Business

TV Fan Business

Fridge/Freezer Business

5:1 1 2 1 9:11a 3 7 1 1 2 11:1 7 1 1 11:2 12 1 1 2 11:5 2 1 1 3 11:7 5 1 3 H:21a 2 1 2 H:21b 1 1 1 Mean: 0.50 4.6 0.89 0.25 0.25 1.9 Chosen value:

0.68 4.6 0.89 0.25 0.25 1.9

Comments: Security Lights: We find it a bit strange that so few interviewed people have reported security lights. One reason could be that many interviews are from the same village (Kisarawe). Another reason could be that Blennow [13] has not always separated security lights from the other lights. We have decided to assign Bar/Restaurant the same number of Security Lights as was calculated for Shop.

4.4.4. Hairdresser Men and Hairdresser Women

Hairdressers generally cut either men or women, but unisex saloons do also exist. As we assume they have the same kind of appliances we have calculated the mean from all the interviewed hairdressers. The exception being Razor, used by Hairdresser Men and Stand Alone Hairdryer, used by Hairdresser Women, see table 4.52.

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Table 4.52. Calculations for Hairdresser Men and Hairdresser Women

Int. no. Security Light

Light Day

Light Business

Radio Business

Fan Business

Razor Stand Alone Hairdryer

6:5 1 2 2 1 1 1 7:8 1 1 1 8:6 1 1 1 9:15 1 2 1 1 1 H:7 2 6 1 1 1 H:15 2 1 1 2 1.2 9:16a 2 1 1 2 H:16 3 1 2 H:22 1 1 3 Mean: 0.40 0.40 1.4 0.78 0.89 2.3 Chosen value:

0.40 0.40 1.4 0.78 0.89 1.2 2.3

Comments: Security Lights, Light Day and Light Business: Blennow [13] has not always separated security lights from other lights. In interview (H:16) and (H:22) he has stated that the hairdressers have lights, but not of what kind. Hence (H:7), (H15), (H:16) and (H:22) are excluded from the calculations of those loads.

4.4.5. Tailor

Tailors are fairly common in Tanzania. Our experience does not agree with Blennow’s [13] result. When we have been out on field visits we have never come across a tailor using an electric sewing machine. Even if they were electrified they preferred the old pedal powered type, perhaps because a new sewing machine as well as electricity are too expensive. Unfortunately we overlooked interviewing these tailors. Calculations for Tailor:

Table 4.53. Calculations for Tailor

Int. no. Security Light

Light Business

Radio business

Fan Business

Iron Tailor

Sewing Machine

H:8 1 1 1 1 1 H:17 1 1 1 1 1 H:18 1 1 H:19 1 1 1 1 1 Mean: 0.25 0.75 0.75 1.0 0.75 0.75 Chosen value:

0.68 0.75 0.75 0.50 0.75 0.50

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Comments:

Security Light: According to our field knowledge, security lights are very important. We do not believe that tailors have less security light than e.g. Shops (0.68). We have therefore adjusted this number. Fan Business: We see no reason for this figure being so very high when e.g. Shop has 0.16 fans. On the other hand we suspect that they are hotter (the iron generates heat and pedalling is hard work) and wealthier than shops. We choose to assign 50 % of the tailors a fan. Sewing Machine: Most of the tailors we have encountered ourselves do not have an electric sewing machine (see above). The mean value of 0.75 seems a little bit too high and we have therefore reduced it to 0.5.

4.4.6. Mosque

Since 30 % of the population in Tanzania is Muslims [2] there are many mosques. Together with the churches they are usually the prettiest and comparatively the most lavish building in the village. Muslims are expected to pray five times per day sometimes starting as early as four o’clock in the morning. In table 4.54. are the calculations for Mosque.

Table 4.54. Calculations for Mosque

Int. no. Security

Light Light Household

Fan Business

Amplifier Loudspeaker Amplifier/ Loudspeaker

7:18 1 19 3 1 2 8:1 1 13 8:8 2 6 1 3 9:6 3 7 1 2 H:37 2 2 1 2 Mean: 1.8 9.4 0.60 0.80 1.80 Chosen value:

1.8 9.4 0.60 1.0

Comments: Light Household: Since some of the mosques use light in the morning and all in the evening we assign the load Light Household to this customer type. Fan Business: Fans are only used during the day, hence we have assigned the load Fan Business to the mosques. Amplifier/Loudspeaker: We have merged the amplifier and loudspeaker to one load, see chapter 4.2.15.

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4.4.7. Church

About 40 % of the Tanzanians are Christian, the greater part Roman Catholics [2]. As Christians attend Mass only once a week and they have fewer practical rules to conform to, compared to Muslims, Christianity is less manifest in the Tanzanian society. They are though very religious and on Sundays they all gather up to go to church. Many times we were asked of what faith we were and it was very difficult for the inquirer to grasp the idea that we did not believe in a God. The result for Church is:

Table 4.55. Calculations for Church

Int. no. Security Light

5:5 3 9:7 6 9:10 7 H:36 8 Mean: 6.0 Chosen value:

6.0

Comments: Apart from security lights churches often have lights and fans, but since these are only used during service on Sunday or on special occasions our general customer type Church has no such loads.

4.4.8. Mill

Mill is principally a load, but can also be viewed as a customer type. We have defined Mill Low Season, Mill Intermediate Season and Mill High Season as loads. Among the records for customer types in the computer program you will find the customer type Mill Low Season allotted one load; Mill Low Season, Mill Intermediate Season allotted one Mill Intermediate Season and so on. The idea is that the user of the program should go into the building housing the mills and count the number of machines. The reason for this somewhat different approach is that a milling machine is a very big load and the number of machines in a milling house varies from one to about three. The user has no way of knowing by just looking at the building how many machines it contains and consequently there would be a high risk for large miscalculations. It is furthermore much easier to count the number of milling machines than for example carpentry machines or appliances in a household.

4.4.9. Carpentry

Unlike the approach for mills we defined four different carpentries: Low Load Carpentry, Medium Load Carpentry and High Load Carpentry. For the five carpentries that we had complete information on both power and user times we computed the electricity consumed per day, reduced the power according to chapter 4.3. and also took into account that about half of the time the machines are on they run without load. We found it to be extremely varied, ranging from 0.21 kWh/day to more than 62 kWh/day, see table 4.56.

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Table 4.56. Calculations for the observations of carpentries

Int. no. Energy Consumption [kWh/day]

7:17 0.21 10:3 0.51 6:6 2.9 9:17 34 9:9 62

We decided to not take into account smallest and largest carpentry as the smallest one was really tiny and had just opened and largest one was situated in very busy area that could hardly be called rural, not even peri-urban. Then we picked and chose from our already defined engines 1 – 4 three suitable combinations to cover the remaining range of energy consumption and got this result:

Table 4.57. Calculations for the customer types Carpentries

Customer Type

Electric Engine 1

Electric Engine 2

Electric Engine 3

Electric Engine 4

Energy Consumption

Low Load Carpentry

1 0.68 kWh/day

Medium Load Carpentry

1 1 1 7.5 kWh/day

High Load Carpentry

1 1 3 1 38 kWh/day

4.4.10 Workshop

We found from a general point of view two types of craftsmen, the ones who work with wood and the ones working with metal. We defined businesses with machines like saws and levelling machines as carpentries and businesses mainly using a weld as workshops. These presumptions are in no way definite as there are businesses owning woodworking machines as well as a weld, but they seem to agree rather well. Many workshops also have a battery charger. When modelling workshops we used the same approach as for carpentries; we calculated the electricity consumption, see table 4.58, and picked suitable loads from our predefined welds, engines and car battery charger to cover the full range of electricity consumption among the interviewed workshops.

Table 4.58. Calculations for the observations of workshop

Int. no. Energy Consumption [kWh/day]

6:10 1.5 9:12 2.1 9:13 6.3 8:5 13 6:9 17 6:3 19 9:18 26 11:4 27

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We decided on three carpentries: Low Load Workshop, Medium Load Workshop and High Load Workshop, see table 4.59.

Table 4.59. Calculations for the customer types Workshops

Customer Type Weld 1 Weld 2 Weld 3 Electric

Engine 1Electric Engine 3

Charger Car Battery

Energy Consumption

Low Load Workshop

1 1 1 2.3 kWh/day

Medium Load Workshop

1 1 1 10 kWh/day

High Load Workshop

1 1 1 27 kWh/day

4.4.11. Filling Station

Along the busy roads filling stations are rather common. An interesting observation we made was that almost all vehicles, especially the small busses called Dala Dala that are used for public transport, refuelled for a very short time, sometimes for not more than 20 seconds. In table 4.60. the calculations for Filling Station are found.

Table 4.60. Calculations for Filling Station

Int. no. Security Light

Light Day

Light Late Business

Radio Business

Fridge/Freezer Business

Pump Filling Station

4b:1 10 1 3 11:9 12 2 H:26 2 1 H:34 10 1 6 1 1 3 Mean: 8.5 0.25 1.5 0.50 0.25 2.3 Chosen value:

8.5 0 0 0.50 0.25 2.3

Comments: Light Day: The use of Light Day is neglected since it is not common to use lights during the day. Light Late Business: As the Security Lights are turned on when gets dark we do not assign this customer type any Light Late Business.

4.4.12. Dispensary

Dispensaries can be either a Governmental or private dispensary. They are smaller institutions where you can get basic medical services like health advice, medicines and simple diagnoses. Even if dispensaries are fairly common most people do not get the medical treatment they need as they can not afford it. There is no doctor and sometimes

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they are so poorly equipped that all they have is a security lamp. Dispensary will be assigned the following appliances:

Table 4.61. Calculations for Dispensary

Int. no. Security Light

Light Business

Fan Business

Fridge/Freezer Household

2:1 2 6 7:5 5 3 1 9:2 6 4 2 H:29 6 12 6 1 H:33 1 Mean: 4.0 5.0 1.2 0.80 Chosen value:

4.0 5.0 1.2 0.80

Int. no. Sterilizer12 Air conditioner12

Microscope13 Communication Radio13

2:1 7:5 9:2 1 1 1 H:29 2 H:33

Int. no. Big Health Appliance

Small Health Appliance

2:1 7:5 9:2 1 2 H:29 2 H:33 Mean: 0.60 0.40 Chosen value:

0.60 0.40

4.4.13. Health Centre

Like dispensaries health centres can be either Governmental or private, but are bigger and with better services. There is usually a doctor present and sometimes they even perform surgical operations. We visited a very large and nice health centre with many buildings and a garden. It was Catholic. Unfortunately they did not have many patients as most people are not able to pay for the service. The calculations for Health Centre are found in table 4.62 and 4.63.

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Table 4.62. Calculations for Health Centre

Int. no. Security Light

Light Day

Light Business

Fan Business

Fridge/Freezer Household

2:2 20 6 2 9:1 2 1 H:1 2 Mean: 7.3 2.0 1.7 Chosen value:

7.3 1.5 10.0 6.0 1.7

Comments: Light Day, Light Business and Fan Business: When we interviewed (9:1) we did not ask about fans and lights but we saw that they had many of each. We also believe that the health centre investigated by Blennow [13] had fans and lights. Therefore we have used our field knowledge and modified the numbers. Our interviewees have not stated any day light use, but since they at least have an office or examination room we have assigned 1.5 Light Day, the same as customer type Office.

Table 4.63. Calculations for Health Centre Int. no.

Sterilizer12 Air Conditioner12

Microscope13 Cooker/Boiler12

Commu-nication Radio13

Shake Machine13

Sucking Machine13

Dryer for tools/ Oven12

2:2 4 1 1 1 1 1 1 9:1 1 1 1 H:1 1 2 1 1

Int. no. Big Health Appliance

Small Health Appliance

2:2 7 3 9:1 2 1 H:1 3 2 Mean: 3.3 2.7 Chosen value:

3.3 2.7

4.4.14. Guesthouse

Guesthouse is the term used in rural Tanzania for a hotel. They usually have some kind of restaurant where you can order traditional Tanzanian meals. We could have separated these observations into Guesthouse and Bar/Restaurant, but we decided not to do so as

12 Big Health Appliance 13 Small Health Appliance

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we at the stage of interviewing (5:6) had not considered this issue and consequently obtained the information as one unit. Blennow [13] did not separate the information either. The assigned appliances:

Table 4.64. Calculations for Guesthouse Int. no. Security

Light Light Household

Light Day

Light Late Business

Radio Business

TV

Fan Household

Fan Business

Fridge/Freezer Household

5:6 7 20 11 1 10 1 9:11a+b 3 7 1 6 1 7 1 2 H:25 4 5 1 4 1 Mean: 3.3 10.3 0.33 7.3 0.33 0.67 7.0 0.33 1.3 Chosen value:

4.0 10.3 0 7.3 0.33 0.67 7.0 0 1.3

Comments: It is obvious that guesthouses experience variation in number of guests at weekends and due to seasons. Therefore the number of each load in the guest rooms has been reduced (before they were entered into the table). Security Light: We have adjusted this number since we believe that interview (H:25) also have security lights even though this is not reported.

Light Day: The use of Light Day is neglected since it is not common to use lights during the day. Light Late Business: The number is slightly higher than that for Bar/Restaurant. This is reasonable since guesthouses often are larger businesses than Bar/Restaurants. Fan Business: All use of fans in guesthouses is assumed to be in the guest rooms and therefore only Fan Household has been used.

4.4.15. Video Show

Since few villagers can afford a TV there is a demand for some kind of entertainment as well as communication with the world. Consequently video shows have arisen. A video show is a place where one can watch videos and TV broadcast and we also imagine that it is a place where you gather around for some socializing, exchanging gossip and news. One household we visited also ran a video show and for this they used their own TV, carrying it back and forward every day. The computations for Video Show are:

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Table 4.65. Calculations for Video Show

Int. no. Security Light

Light Late Business

TV Video Show

Fridge/Freezer Business

Video

7:4b 1 1 H:31 2 1 1 1 H:32 1 1 Mean: 0.67 1.0 0.33 1.0 Chosen value:

0.67 1.0 1.0 0.5 1.0

Comments: Light Late Business: Common sense and our field knowledge say that the video show should have at least one lamp lit in the evening. Fridge/Freezer Business: We believe that at least every second video show has gone into the lucrative business of selling cold drinks.

4.4.16. Secondary School

Education is a prioritized sector in Tanzania. Primary school is compulsory and found in all villages. They are normally not electrified though and therefore not entered as a customer type. Secondary Schools can be electrified and the appliances that can be encountered are:

Table 4.66. Calculations for Secondary School

Int. no. Security

Light Light Day

Light Business

Fan Business

Fridge/Freezer Household

Big Health Appliance

Computer Printer

H:30 27 7 15 1 1 1 1 Chosen value:

5.0 15 3.0 10.0 1.0 1.0 1.0 1.0

Comments: Unfortunately we only have one interview for this customer type. Hence we will modify this customer type according to our field knowledge.

Security Lights: The customer type is also most likely to have some Security Lights.

Light Day and Light Business: In big customer types like this the number of lights that goes into the computer program should be smaller than the real number of appliances because some lamps are likely to be either not used or broken.

Fan Business: See Light Day and Light Business.

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4.4.17. Office

We and Blennow [13] have encountered different institutions that are very similar and therefore suitable to be merged into one load: they provide some kind of service and use very little electricity; all tasks are done by hand. The ones we have interviews from include Governmental office, post office, police station and a financial institution, see table 4.67.

Table 4.67. Calculations for Office

Int. no. Security

Light Light Day

Light Business

7:22 1 2 2 H:23 3 2 H:24 1 2 H:38 1 Mean: 1.5 1.5 0.50 Chosen value:

1.5 1.5 0.50

4.4.18. Secretary Service

As computers are non-existent among households and business in the countryside there is a need for printing and photocopying services in busier areas. These businesses are called secretary services. We visited a very enterprising woman who had both a secretary service and a hair saloon, but we do not think these businesses in general are as wealthy as hers. How we have defined Secretary Service is found in table 4.68.

Table 4.68. Calculations for Secretary Service

Int. no. Security

Light Photocopier Computer Printer

9:16b 1 1 1 Chosen value:

1.0 1.0 0.2 0.2

Comments:

Security Light: A business owning expensive appliances is most likely to have a Security Light.

Photocopier: We have encountered quite a few photocopiers.

Computer and Printer: There are also some businesses of this kind that provide more advanced services, like editing and printing. We believe that the one out of five of this customer type is of the advanced type.

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5. Load Estimation Tool

5.1. FileMaker Pro FileMaker Pro is the database software that we have chosen for the creation of our Load Estimation Tool. A database is a collection of data that you can organize, update, sort, search through and print as needed. The main reason for us choosing this software is that the database environment makes it easy to enter, store and access data, but at the same time it contains a very important feature: the possibility to write scripts (programming). It is not as easy to program as for example MATLAB (the MathWorks) and has occasionally given us a headache, but with some few compromises we have managed to solve most problems in the end.

5.2. Computer Program This Load Estimation Tool is a basic and very simple to use computer program that can be used to analyze the potential electricity use in non-electrified villages. It aggregates loads according the Load Estimation Model that we have developed and described in chapter 4.1. It is adapted to Tanzanian conditions and the inputs are the number of each customer type in the village. The output is a vector of expected power for each hour. Moreover, the user can easily add new loads and customer types. The tool comes with a user guide. The Load Estimation Tool consists of four levels: Load, Customer Type, Village and Design. At each level you can generate the expected power and the variance by running the program, calculated according to chapter 4.1.3. An additional feature in Customer Type, Village and Design is the three fields that displayed the electricity consumption (kWh) for a weekday, Saturday and Sunday. At the level Load you will find the electric appliances and machines that we defined in chapter 4.2. For each load the power and the probability vector are specified. At the level Customer Type you find the different customer types modelled according to chapter 4.4. and consequently it contains information about the number of each load. At the level Village you can insert the number of each customer type in a village. Even if a village is electrified there are always people who can not afford to be connected. If you only know the total number of people in a village there is a possibility in the program to set the “connection rate” and with that get a rough estimate of the expected power and variance.

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At the level Design you state which villages you want to include in your calculations. By specifying the α-quantile, as explained in chapter 4.1.3., you will get a measure of the maximum power outtake. A user guide is enclosed in the Load Estimation Tool. You can also find it in appendix F.

5.3. Model Validation The Load Estimation Model is based on interviews and our field knowledge. Measurements on a whole village or even for a few households in Tanzania have been hard to get. Frances [14] has performed measurements on a village in Uganda, Najjeera, a few kilometres from the capital Kampala. To validate our Load Estimation Model we have compared her measurements with the output from our model. The available measurements are from domestic users only, why no productive users like mills and workshops are included in the validation. Measurements on each phase on four different transformers are available. Figure 5.1. displays a 24 hour profile for one of these phases. The connected customers to this phase are five households.

Figure 5.1. Measured 24 hour profile of the electricity consumption of five households

in Najjeera [14].

These five households in Najjeera are well documented in interviews made by Frances [14]. Since the input has to be either Low-, Medium- or High Load Household this is important when defining the inputs to the model. If we look at the number of lights; four out of the five households indicate that they would belong to a category that is higher then medium but a little less than high. Also the number of radios indicate that they would belong somewhere between medium and high. The number of TVs agrees well with our medium category while the number of iron indicates that they are all high. The loads fridge and fan point towards somewhere just below medium. If three High Load Households and two Medium Load Households are chosen as the input to the Load Estimation Tool we get the result of figure 5.2.

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Figure 5.2. Output from the Load Estimation Tool, 24 hour profile of three

High Load Households and two Medium Load Households

The shape of the profile is very similar apart from that the peak in the morning is much less evident in our Load Estimation Model. Also the amount of electricity calculated by the computer program is smaller: 26.4 kWh/day (Frances [14]: 32.3 kWh/day). This might suggest that our Load Estimation Model underestimates the electricity use of households. The energy calculated by the computer program only accounts for 82 % of the measured energy. The dotted line above the continuous line is the maximum power outtake that will not be exceeded with a probability of 0.99 (α-quantile = 0.010). Here is another example: In figure 5.3. the power is plotted for nine households in Najjeera over many days. These households are situated in the centre of the village where the commercial activity is higher.

Figure 5.3. Measured 24 hour profile (total 96.4 kWh/day) of the electricity consumption

of nine households in Najjeera [14].

For the number of lamps these households have a higher mean than the households in the survey above and this indicates the category High. The number of TVs, radios, fans and

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fridges indicates Medium while the number of iron indicates High. The use of appliances like cookers, kettles, a video and a computer is also widespread in these nine households and imply an even higher category than High. Even though some appliances indicate only the Medium category we believe that the input to the Load Estimation Model should be at least High Load Households. Since the commercial activity is high on this phase and since we believe that at least one or two of these households run some kind of commercial activity we add two shops. Shops are counted as a separate activity in the Load Estimation Model even if they are under the same roof as a household, which is very common. The result can be seen in figure 5.4.

Figure 5.4. Output from the Load Estimation Tool, 24 hour profile (total 62.9 kWh/day)

of nine High Load Households and two Shops

In this case the computed energy only reaches 65 % of the measured energy. We suspect that these nine households are bigger consumers of electricity than even our High Load Household. Especially as they own and use cookers, an appliance with a high power, which is something we did not come across during our filed visits in Tanzania and that is not included in the Load Estimation Model. The big difference in this case was therefore expected. The aim when creating this Load Estimation Tool has been to adapt it to low cost electrification, suitable for rural Tanzania. When we have read Frances’s [14] report we have realized that the villages she has visited have been more developed than the villages that have been our target. She also writes that the proximity to Kampala results in a higher standard of living than in other rural areas. Some small losses between the point of measuring and the point of consumption can also affect the measurements to be slightly higher than actual consumption in the ideal case. Since we lack measurements for a village that is situated in the target region any real conclusions are hard to make. Even though we generally believe in the accuracy of our Load Estimation Model, we suspect that for households the consumption might be underestimated. One of the conclusions in the work by Frances [14] is that Load Estimation Models just based on interviews can generate lower consumption levels than the actual values. If this is the case for our model we do not believe that the

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underestimation of the household consumption exceeds 20 %. Note that we only talk about the consumption for households and not for productive users.

5.4. Socio-economic Analysis Manda and Daluni are two villages that we visited on our first and second field visit. As an example of how the Load Estimation Tool can be used we have run these villages in the computer program and the result is presented below. The number of households that will be electrified is not the same as the total number of households. In Manda there are 745 households and in Daluni there are 600. The connection rate is usually low and essentially depends on the wealth of the village. When we visited the villages we tried to categorize them after their economic development. This is of course very difficult, but we did it to the best of our ability. Manda was rated very low while Daluni belonged to a high category. For this example we assume that 40 % of the households in Daluni will be electrified and only 30 % in Manda. We also have to make an assumption of how many households that will be low-, medium- or high load households. For this we use the information that in Daluni 42 % of the households had iron roof while the corresponding figure for Manda is only 8 %. All other customer types are considered to be electrified. The final input to the computer program is shown in table 5.1.

Table 5.1. Input for the villages Manda and Daluni

Customer Type Manda Daluni Low Load Household 110 48 Medium Load Household 88 120 High Load Household 22 72 Shop 13 15 Mosque 1 2 Church 0 5 Mill Intermediate Season 4 8 Medium Load Workshop 0 1 Dispensary 1 1 Guesthouse 0 1 Video Show 0 5

The output from the model can be seen in the figures 5.5. and 5.6. The lower curve (continuous blue) is the expected power for each hour and the curve above (dotted red) is the maximum power outtake that will not exceeded with the probability 0.975 (α-quantile = 0.025).

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Figure 5.5. Output from the Load Estimation Tool for Manda

Figure 5.6. Output from the Load Estimation Tool for Daluni

Even though the villages have about the same number of households we see that the more developed Daluni would consume much more electricity if it was electrified. It is also interesting to see that the variation is much higher during the day when the mills are

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running. The risk for high power outtake is especially large around 19 pm when the use of light has started and some mills are still running. The model also gives information about the change of energy consumption during the week. Table 5.2. shows this information.

Table 5.2. Energy consumption for the villages Manda and Daluni

Day Daluni [kWh] Manda [kWh] Weekday 1575 881 Saturday 1424 805 Sunday 1272 728

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6. Discussion and Future Work

Through out our work we have given probabilities and number of appliances with an accuracy of as much as two decimals. Since our loads and customer types are sometimes created on few observations and sometimes only to the best of our abilities we can by no means say that the accuracy is that high. At the end of our stay in Tanzania a presentation of the Load Estimation Model was held. Present were people from DIT and IPS. They showed great interest in the model and asked a lot of questions. Even though we generally believe that the Load Estimation Tool produce correct values for expected power and variance, we suspect that the electricity consumption for households might be underestimated. One of the conclusions in the work by Frances [14] is that Load Estimation Models just based on interviews can generate lower consumption levels than the actual values. Through model validation, of which only a part is presented above, we do not believe that the underestimation of the households’ consumption exceeds 20 %. Since we lack measurements for a village that is situated in the target region any real conclusions are difficult to make. One suggestion for future work is to validate the model properly. With this we mean measuring and logging the power to a confined area in rural Tanzania where each consumer can be visited and investigated to establish the correct inputs to the Load Estimation Tool. It would be favourable if this area included some of the productive customer types like mills or workshops. No validation has yet been made to determine how accurately the Load Estimation Model treats these loads. The loads and customer types could then be modified according to the results of the measurements. The Load Estimation Tool could be further developed into a comprehensive planning tool. This planning tool could give additional information about the cost per connection along with required electricity price through the choice of cables and transformers. An elaborate model could be created where different qualities and lengths of cables as well as different sizes of transformers can be chosen. Another feature that could be added to the Load Estimation Tool is the ability to forecast the increase in electric energy consumptions over a period of years. The socio-economic potential of the area must then be related to some kind of parameters, for example distance to a main road, type of agriculture, income and entrepreneurial spirit.

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7. References

[1] the United Republic of Tanzania (17-06-2005), http://www.tanzania.go.tz. [2] NE, volume 18 (1995), Bokförlaget Bra Böcker AB, Höganäs. [3] Tanzania Petroleum Development Corporation (06-08-2005), http://www.tpdc-tz.com. [4] M. Cuellar; E. Dahlström; H. Peterson (1997), Swedish Support to Tanzania's Power Sector, SIDA, Stockholm. [5] Tanesco (20-06-2005), http://www.tanesco.com. [7] Edward E. Marandu (2002), The prospects for local private investment in

Tanzania’s rural electrification. Energy Policy, 30, p. 977–985.

[8] StonePower AB (20-06-2005), Cost effective electrical networks, http://www.stonepower.se/Lowcostnetworks.htm.

[9] Decon; Sweco; Inter-consult (2005), Tanzania Rural Electrification study

Institutional Analyses – Interim Report (Phase 1). [10] Edward E. Marandu (2004), Licensing Laws and Implications for Private

Investment: The Case of Tanzania, Blackwell Publishing Ltd, Oxford. [11] Feinstein C (2001), Economic Development, Climate Change, and Energy

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http://www.un.org/millenniumgoals. [13] Blennow, Henrik (2004), Methods for Rural Load Estimation. [14] Sprei, Frances (2002), Characterization of power system loads in rural Uganda. [15] Bergman, Sten, President of StonePower AB [16] Masika Dr. R. J., Vice Principal and Director of Studies at Dar es Salaam

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Appendix

These can be found in a separate file

A Interviews

B Interviews by Blennow [13]

C Calculations for Loads

D Observations by Appliance

E Weekly Variations

F User Guide