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