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Assessment of dataneeds and capacity
Outline
IRENA statistical activities
Improvement of renewable energy statistics- Barriers to data collection- Main steps to improving statistics
Data audit (discussion)
IRENA statistical activities
Datacollection
IRENAquestionnaire
Secondarydata sources
Datadissemination
Internal database(working system)
External database(publications
and REsource)
Capacitybuilding
Renewable energystatistics papers
Training coursesand materials
Data collection
IRENA questionnaire:- Only global data collection for renewables- Focused on renewables (including off-grid)- Detailed renewable energy data- Consistent with other energy surveys and
they can be used to complete it
Additional data obtained from official sources, trade sources and estimation
Publications:- Capacity statistics 2000-14- Production and balances
IRENA REsource:- Data query/download- Time-series charts- Country comparisons- Maps- Pie charts http://resourceirena.irena.org
Data dissemination
Publications:- Bioenergy and distributed renewable energy - Ren. energy classification and definitions- Bioenergy data collection guidelines
Training workshops:- UAE- Southern Africa (Swaziland)- ….others
Capacity building
The role of statistics
Energy Statistics
Assess baseline
Set targets
Energy plans and policies
Financing
Monitor progress
Measure impacts
You can’t manage what you don’t measure!
Data collection challenges
1. Institutional + human resource capacity
- Renewable energy production is dispersed, making data collection difficult, with many institutions invovled
- Lack of coordination between institutions - A need for formal and/or informal mandates
and data sharing agreements
Data collection challenges
- No means to collect, store and share data on renewables
- Lack of institutional memory, archiving and documentation processes
- Limited staff capacity and experience with renewable energy statistics
- Lack of financial resources
Data collection challenges
2. Technical challenges
- Lack of clear definitions and classification of products and flows
- Need for agreed and practical sampling frameworks and methods of estimation
- Emerging technologies need to be captured in energy statistics (e.g. hybrid plants)
- Measurement issues (e.g. wind capacity)
Data collection challenges
- Off-grid: Need for guidance to measure distributed uses more effectively
- Bioenergy: bioenergy presents numerous methodological challenges for survey design, measurement and validation, particularly with respect to traditional uses of biomass
Data collection challenges
3. Timeliness of data collection
- It is extremely difficult for statistics to keep up with the rapid evolution of renewable energy
Main steps to improvement
Challenges are numerous - we need to be targeted and systematic about the data we collect and the processes we use
Define data needs
Conduct data audit
Develop reporting templates
Strengthen institutional frameworks
Develop data
collection methods
Review and validate
dataDisseminate
data
Identify and implement
improvements and build capacity
1. Define data needs
Identify the scope of activities to monitor:Define activities that should be tracked based on national context and priorities, for example: - A renewable energy target - A national energy plan or policy- Renewable energy investments and projects- Energy access? Energy security?- Affordability? Long-term energy supply?
1. Define data needs
Relevant data collection efforts could include:- Electricity capacity and generation (grid-
connected and off-grid)- Small-scale systems in specific areas
(e.g. desalination, water pumps etc.)- Social, economic, environmental impacts
(e.g. jobs, emissions, trade balance etc.)- Full energy balance (supply, transformation,
consumption by end-uses)
2. Data audit
Assess existing data and identify gapsAssess existing data collection activities:- Identify all organisations that may already
be collecting data, such as: government agencies; utilities; industry associations
- See what data is already being collected and identify remaining data gaps
2. Data audit
Simplified example of an assessmentFlow Electricity
(on grid)Electricity(off grid)
Solar thermal Bioenergy
Productionelectricity company,
regulator, IPPs, industry associations
household surveys, retail surveys, IPPs
household surveys, retail surveys,
planning authorities
household surveys, retail surveys,
agricultural surveys
Trade network operator customs administration (equipment and biofuels)
Supply Calculated
Transformation electricity company, IPPs, industry associations IPPs electricity company,
IPPswood and food
processing enterprises
Losses network operator, distribution company IPPs
Consumption Calculated
Industry
distribution company
enterprise surveys
Households household surveys, market surveys
Services enterprise surveys
Other enterprise surveys agricultural surveys
3. Develop reporting tools
Develop templates reflecting the national data collection priorities: - Use internationally agreed definitions,
boundaries and units for energy statistics - Use standard templates for reporting that
cover major priorities in sufficient detail
4. Institutional frameworks
Develop institutional mechanisms for coordination and data sharing Develop a data collection cycle, to include:- Clear roles and responsibilities- Mechanisms for coordination including data
sharing agreements- Defined data collection, analysis, and validation
processes including agreed timelines- Documented procedures for institutional memory
5. Data collection tools
Identify data collection and estimation methods based on needs and resources Data collection mechanisms could include:- Surveys - Administrative data- Imputation (estimation methods e.g. models)- In situ measurements
Explore whether existing data collection activities could meet the energy data needs
6. Data validation
Create mechanisms for data checking, querying and validation - Develop simple mechanisms to cross-check
the validity of data (negative values, unlikely conversion factors, sudden changes in data)
- Involve stakeholders in the review process to ensure that final numbers are accurate
7. Data dissemination
Create mechanisms for data dissemination - It is important that data is made easily
accessible to all stakeholders- This could include online platforms or
publications such as a statistical yearbook - Release data following a calendar- Considerations: will all data be made
publically available? Are there issues of confidentiality or levels of data access?
8. Continue improvement
Use data to report progress towards targetsAssess gaps and weaknesses: - Timeliness and relevance- Data quality - Efficiency of data collection- Impacts