Investigation on correlation of energy consumption of multi-buildings on campus area

Wang, Wei; Chen, Jiayu

Published in:Energy Procedia

Published: 01/02/2019

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Published version (DOI):10.1016/j.egypro.2019.01.911

Publication details:Wang, W., & Chen, J. (2019). Investigation on correlation of energy consumption of multi-buildings on campusarea. Energy Procedia, 158, 3559-3564. https://doi.org/10.1016/j.egypro.2019.01.911

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ScienceDirect

Available online at www.sciencedirect.comAvailable online at www.sciencedirect.com

ScienceDirectEnergy Procedia 00 (2017) 000–000

www.elsevier.com/locate/procedia

1876-6102 © 2017 The Authors. Published by Elsevier Ltd.Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

The 15th International Symposium on District Heating and Cooling

Assessing the feasibility of using the heat demand-outdoor temperature function for a long-term district heat demand forecast

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

aIN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, PortugalbVeolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France

cDépartement Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France

Abstract

District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heatsales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors.The results showed that when only weather change is considered, the margin of error could be acceptable for some applications(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd.Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

Keywords: Heat demand; Forecast; Climate change

Energy Procedia 158 (2019) 3559–3564

1876-6102 © 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy.10.1016/j.egypro.2019.01.911

10.1016/j.egypro.2019.01.911

© 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy.

1876-6102

Available online at www.sciencedirect.com

ScienceDirect

Energy Procedia 00 (2018) 000–000 www.elsevier.com/locate/procedia

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China

Investigation on correlation of energy consumption of multi-buildings on campus area

Wei Wang a, Jiayu Chena,* a Department of Architecture and Civil Engineering, City University of Hong Kong, Y6621, AC1, Tat Chee Ave, Kowloon, Hong Kong

Abstract

Buildings occupy a large proportion of energy use and to analyze the building energy use is quite important for understanding building energy pattern and energy conservation methods. For multi building energy use analysis, researchers realized the inter- impact and -relationship between multi buildings by considering inter buildings effect and identifying reference buildings in the group. This study would like to investigate correlations between multi buildings to identify the relationship and reference buildings. In the method, the social network technique method was used to identify the reference buildings and correlation between them and total buildings energy use, non-reference buildings, respectively. To validate proposed method, this study selected Southeast University as a case study and two buildings types were tested, including education buildings group and laboratory buildings group. In the results, for education buildings, there are three reference buildings with the correlations between them and total buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between reference buildings and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two reference buildings and two non-reference buildings are 0.632 and 0.613, respectively, and 0.637 and 0.218, respectively. This study can be a significant case study for the interdisciplinary research on multi-buildings energy use analysis studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +852 3442 4696.

E-mail address: jiaychen@cityu.edu.hk (Jiayu Chen)

Available online at www.sciencedirect.com

ScienceDirect

Energy Procedia 00 (2018) 000–000 www.elsevier.com/locate/procedia

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China

Investigation on correlation of energy consumption of multi-buildings on campus area

Wei Wang a, Jiayu Chena,* a Department of Architecture and Civil Engineering, City University of Hong Kong, Y6621, AC1, Tat Chee Ave, Kowloon, Hong Kong

Abstract

Buildings occupy a large proportion of energy use and to analyze the building energy use is quite important for understanding building energy pattern and energy conservation methods. For multi building energy use analysis, researchers realized the inter- impact and -relationship between multi buildings by considering inter buildings effect and identifying reference buildings in the group. This study would like to investigate correlations between multi buildings to identify the relationship and reference buildings. In the method, the social network technique method was used to identify the reference buildings and correlation between them and total buildings energy use, non-reference buildings, respectively. To validate proposed method, this study selected Southeast University as a case study and two buildings types were tested, including education buildings group and laboratory buildings group. In the results, for education buildings, there are three reference buildings with the correlations between them and total buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between reference buildings and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two reference buildings and two non-reference buildings are 0.632 and 0.613, respectively, and 0.637 and 0.218, respectively. This study can be a significant case study for the interdisciplinary research on multi-buildings energy use analysis studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +852 3442 4696.

E-mail address: jiaychen@cityu.edu.hk (Jiayu Chen)

3560 Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564

2 Author name / Energy Procedia 00 (2018) 000–000

Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018).

Keywords: multi buildings, energy use intensity, correlation, social network technique;

1. Introduction

Buildings occupy more than 40% of primary energy usage and become one of the main energy consumers [1], while in cities, buildings can consume up to 75% of total primary energy use [2]. Particularly, electricity contributes as one of the main energy use and the latest Electric Power Monthly with Data for January 2018 reported by Department of Energy (DOE), U.S., indicated the electricity consumption of both commercial and residential buildings consumes 77.5% of all the electricity produced in U.S [3]. The International Energy Agency (IEA)’s Energy in Buildings and Communities (EBC) Programme annexes also highlighted the importance of analyzing total energy use in buildings to reduce energy use and emissions in buildings and communities [4,5].

Besides the single building, more researchers started to realize the inter-impact and –relationship between

building groups and investigate the inter-relationships between multi buildings. Further, the concept of the Inter-Buildings Effect (IBE) was introduced to understand the complex mutual impact within spatially proximal buildings [6–8]. For example, using the case in Perugia, Italy, Han et al explored the mutual shading and mutual reflection for IBEs on building energy performance with two realistic urban contexts [9] and further simulated the IBE on energy consumption form embedding phase change materials in building envelopes [10]. Recently, there are two main approaches applied for energy analysis of multi building groups and they are simulation and cluster method. Some software and web based interface applications, are the novel and creative approaches to analyzing and predicting energy use of multi-buildings in the distributed or urban areas. On one hand, for example, the City Building Energy Saver (CityBES), an Energyplus based web application, provides a visualization platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale efficiency programs [11–13], also predicting energy use for building retrofits measurements. Based on CityBES, Chen analyzed the impacts of building geometry modeling on urban building energy models to understand how a group of buildings will perform together [14]. On the other hand, for clustering method, Deb and Lee [15] studied on determining key variables influencing energy consumption in 56 office buildings through cluster analysis. The clustering approach focuses the investigation on small number of representative, reference buildings from a large buildings dataset [16]. Gaitani et al. [17] applied several variables, heating surface, building age, insulation of the buildings, number of classroom and students, operation hours, and age of heating system, in principal component and cluster analysis method to find the reference buildings.

To expand the studies of IBE, this study would like to use the regression model and social network technique to

investigate on correlation of energy consumption of multi-buildings and define effectively the reference buildings in multi buildings group. Also, this study provides the insights into the multi-building energy prediction based on the social network technique.

2. Case study

This study chose four education buildings and four laboratory buildings, total eight buildings in Southeast University, located in Nanjing City, Jiangsu province, China. The location of eight buildings in Southeast University can be found in Fig. 1. The education buildings are in use for students to have class and study insides, while laboratory buildings are usually used for researchers to conduct their experiments in need. To apply social network

Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 3561 Author name / Energy Procedia 00 (2018) 000–000 3

technique, this study selected monthly building energy use intensity dataset from year 2015 to year 2017 and the energy use intensities for different buildings were calculated by building electricity use data and building area.

Fig. 1 The layout of eight buildings in Southeast University.

Commonly there are the two main approaches to build the connections of individual building in social network technique, they are distance method (e.g. Euclidean distance), which is usually used to calculate difference between two objects, and correlation method (e.g. Pearson correlation coefficient), which is usually used to find the similarity between each. This study used the Pearson correlation coefficient method to calculate the connections between buildings. Then, two steps are taken to extract the networks. The first step is to identify the reference buildings. The second step can build networks between all buildings and exclude weak networks by setting one threshold.

3. Results

3.1 Results on building energy use intensity

This subsection shows the energy use intensity results of education buildings and laboratory buildings, which are indicated in Fig. 2 and 3. In Southeast University, those education buildings are relatively newly built around year 1980 to 1990. For education buildings, the EUIs are usually smaller around two summer break months around July and August and one winter break month around February. The EUI varies from 1.06 to 3.11 kWh/m2, from 0.97 to 4.86 kWh/m2, from 0.79 to 3.22 kWh/m2, respectively for education building 1, 2, and 4. While the education building 2 was the biggest energy consumer, the EUI of which varies from 3.63 to 14.34 kWh/m2.

3562 Wei Wang et al. / Energy Procedia 158 (2019) 3559–35644 Author name / Energy Procedia 00 (2018) 000–000

Fig. 2. The building EUI results of education buildings group (year 2015: left, year 2016: middle, year 2017: right).

Fig. 3. The building EUI results of laboratory buildings group (year 2015: left, year 2016: middle, year 2017: right).

The laboratory buildings energy use intensities for three years generally show unclear trend. The average EUI for L4 is quite smaller than EUIs for others and its EUI varies from 0.04 to 6.01 kWh/m2 and average EUI is about 0.89 kWh/m2. Although L3 has the similar EUI trend to L4, varying from 0.16 to 7.27 kWh/m2, the average EUI of L3 is around 4.75, which is far higher than it of l4. The L2 consumes the biggest energy and also has the largest building area. Its EUI varies from 6.36 to 11.4 kWh/m2 and average EUI is about 8.48 kWh/m2. While for L1, its EUI is from 0.72 to 10.87 kWh/m2 with average EUI of 3.58 kWh/m2.

3.2 Results on building networks

This subsection discusses the applications and results of the social network analysis. Fig. 4 and 5 include the networks between buildings for two buildings groups by calculating the correlation of total building EUI in year 2015 to 2017. In the figures, blue color circle represents the energy use intensity of non-reference building, yellow color circle represents the energy use intensity of reference building, and the green color circle represents the energy use intensity of total buildings. For education buildings group, only building E4 is the non-reference buildings. The building with most relevant trend to total buildings EUI trend is the building E2 with highly correlation of 0.983. The building E3 is also highly correlated to the total building EUI trend and its correlation is 0.91. While considering the networks in laboratory buildings, building L1 and L2 are the reference buildings with correlations of 0.722 and 0.918, respectively. For non-reference buildings, the building L4 is negative relevant to both buildings L1 and l2, showing the opposite EUI trends between L4 and L1, L2, respectively. Meanwhile the building L3 also shows the much low relation of EUI trend to reference building L1 and L2.

Wei Wang et al. / Energy Procedia 158 (2019) 3559–3564 3563 Author name / Energy Procedia 00 (2018) 000–000 5

Fig. 4 The results of networks between education building group.

Fig. 5 The results of networks between laboratory building group.

4. Discussion and conclusion

This study proposed a methodology by investigating correlations between multi buildings. In the method, the social network technique method was used to identify the reference buildings and correlation between them and total buildings energy use, non-reference buildings, respectively. To validate proposed method, this study selected Southeast University as a case study and two buildings types were tested, including education buildings group and laboratory buildings group. In the results, for education buildings, there are three reference buildings with the correlations between them and total buildings energy use intensities about 0.712, 0.983, and 0.910. While the correlations between reference buildings and non-reference buildings are 0.814, 0.845, and 0.741. For laboratory buildings, the correlations between reference buildings and total building energy use intensity are 0.722 and 0.918, while the correlations between two reference buildings and two non-reference buildings are 0.632 and 0.613, respectively, and 0.637 and 0.218, respectively.

This study can be a significant case study for the interdisciplinary research on multi-buildings energy use analysis studies. The reference buildings play a very important role in analyzing multi buildings group by providing the insights for, (1) identifying the buildings with key contributions to total building energy use intensity; (2) representing non-reference buildings when analyzing multi-building energy use with less building information; (3) integrating networks between reference buildings, total building energy use and non-reference buildings, respectively into other disciplines, for example, future study can apply the reference buildings to predict total building energy use.

3564 Wei Wang et al. / Energy Procedia 158 (2019) 3559–35646 Author name / Energy Procedia 00 (2018) 000–000

Acknowledgement

The work described in this paper was sponsored by the project JCYJ20150518163139952 of the Shenzhen Science and Technology Funding Programs and the National Natural Science Foundation of China (NSFC #51508487). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the Science Technology and Innovation Committee of Shenzhen and NSFC.

References [1] Pérez-Lombard L, Ortiz J, Pout C. A review on buildings energy consumption information. Energy and

Buildings 2008;40:394–8. doi:10.1016/j.enbuild.2007.03.007. [2] City Energy Project | A Joint Project of NRDC + IMT n.d. http://www.cityenergyproject.org/ (accessed May

3, 2018). [3] U.S. Energy Information Administration (EIA). Electric Power Monthly with data for January 2018.

Washington, DC: 2018. [4] Hong T. IEA EBC annexes advance technologies and strategies to reduce energy use and GHG emissions in

buildings and communities. Energy and Buildings 2018;158:147–9. doi:10.1016/J.ENBUILD.2017.10.028. [5] Yoshino H, Hong T, Nord N. IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation

methods. Energy and Buildings 2017;152:124–36. doi:10.1016/J.ENBUILD.2017.07.038. [6] Han Y, Taylor JE, Pisello AL. Toward mitigating urban heat island effects: Investigating the thermal-energy

impact of bio-inspired retro-reflective building envelopes in dense urban settings. Energy and Buildings 2015;102:380–9. doi:10.1016/J.ENBUILD.2015.05.040.

[7] Pisello AL, Castaldo VL, Taylor JE, Cotana F. Expanding Inter-Building Effect modeling to examine primary energy for lighting. Energy and Buildings 2014;76:513–23. doi:10.1016/J.ENBUILD.2014.02.081.

[8] Pisello AL, Taylor JE, Xu X, Cotana F. Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions. Building and Environment 2012;58:37–45. doi:10.1016/J.BUILDENV.2012.06.017.

[9] Han Y, Taylor JE, Pisello AL. Exploring mutual shading and mutual reflection inter-building effects on building energy performance. Applied Energy 2017;185:1556–64. doi:10.1016/J.APENERGY.2015.10.170.

[10] Han Y, Taylor JE. Simulating the Inter-Building Effect on energy consumption from embedding phase change materials in building envelopes. Sustainable Cities and Society 2016;27:287–95. doi:10.1016/J.SCS.2016.03.001.

[11] Building Technology and Urban Systems Division at Lawrence Berkeley National Laboratory; City Building Energy Saver n.d. https://citybes.lbl.gov/.

[12] Chen Y, Hong T. Creating Building Datasets for CityBES 2017. [13] Chen Y, Hong T, Piette MA. Automatic generation and simulation of urban building energy models based

on city datasets for city-scale building retrofit analysis. Applied Energy 2017;205:323–35. doi:10.1016/J.APENERGY.2017.07.128.

[14] Chen Y, Hong T. Impacts of building geometry modeling methods on the simulation results of urban building energy models. Applied Energy 2018;215:717–35. doi:10.1016/j.apenergy.2018.02.073.

[15] Deb C, Lee SE. Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data. Energy and Buildings 2018;159:228–45. doi:10.1016/j.enbuild.2017.11.007.

[16] European Commission. Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings by establishing a comparative methodology framework for calculating. 2012.

[17] Gaitani N, Lehmann C, Santamouris M, Mihalakakou G, Patargias P. Using principal component and cluster analysis in the heating evaluation of the school building sector. Applied Energy 2010;87:2079–86. doi:10.1016/J.APENERGY.2009.12.007.