Leveraging Smart System Technologies in National Energy Data Systems: Challenges and Opportunities Eric Masanet Head, IEA Energy Demand Technology Unit EEG Research and Matchmaking Conference Washington, DC, 3-Nov-2016

Typical datasets can include: National energy data systems: overview Typical datasets can include: Energy balances Source: http://www.iea.org/Sankey/

National energy balances Source: http://www.iea.org/Sankey/

Typical datasets can include: National energy data systems: overview Typical datasets can include: Energy balances Energy efficiency indicators   Source: http://www.iea.org/Sankey/

National energy data systems: examples Source: IEA (2014). Energy Efficiency Indicators: Essentials for Policy Making. http://www.iea.org/publications/freepublications/publication/energy-efficiency-indicators-essentials-for-policy-making.html

Typical datasets can include: National energy data systems: overview Typical datasets can include: Energy balances Energy efficiency indicators   Energy-related environmental emissions    Source: http://www.iea.org/Sankey/

National energy data systems: examples Source: (EA (2016) https://www.iea.org/newsroom/news/2016/march/decoupling-of-global-emissions-and-economic-growth-confirmed.html

Typical datasets can include: National energy data systems: overview Typical datasets can include: Energy balances Energy efficiency indicators   Energy-related environmental emissions    Public energy technology research, development, and demonstration (RD&D) investments Source: http://www.iea.org/Sankey/

National energy data systems: examples Source: IEA (2016) https://www.iea.org/statistics/topics/rdd/

Typical datasets can include: National energy data systems: overview Typical datasets can include: Energy balances Energy efficiency indicators   Energy-related environmental emissions    Public energy technology research, development, and demonstration (RD&D) investments Fuel prices and taxes Development indicators (e.g., energy access) With much variation in comprehensiveness and quality from country to country! Source: http://www.iea.org/Sankey/

Sound energy policy requires sound data Myriad end uses in the policy stakeholder system Understanding dependencies, supplies, and security Understanding final demand nature and trends Modeling and analysis of policies and technologies Guiding R&DD programs and technology investments Tracking: energy, climate, and development progress UN sustainable development goals (SDGs) Paris Agreement GHG emissions commitments and reporting Green growth indicators Effectiveness of policies Impacts of investments

Data disaggregation vs. requirements Source: IEA (2014). Energy Efficiency Indicators: Essentials for Policy Making. http://www.iea.org/publications/freepublications/publication/energy-efficiency-indicators-essentials-for-policy-making.html

Disaggregation and progress tracking +15% -35% Index: 1990=1. Data for IEA18 (Australia, Austria, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Slovakia, Spain, Sweden, Switzerland, UK, USA). Source: IEA energy efficiency indicators database. TC: Temperature Corrected.

Greater insights via data matching Percentage change in median gas consumption by UK property type Structure of domestic NEED Source: UK DECC (2016). Summary of analysis using the National Energy Efficiency Data-Framework (NEED)

Dimensions of robust national data systems Possible challenges in developing countries Energy within overall policy priority May be not enough / not in synergy with other objectives Overall data governance Lack of trust / lack of legal framework /lack of enforcement Resources for energy statistics May be poor / ineffectively allocated across areas / competing areas for statistics Institutional arrangements/ flexibility Structural inefficiencies/unawareness of other work/competing priorities for different stakeholders/multiple data requests/lack of data sharing Data collection practices Not effective, well designed/lack of training,/lack of expertise/not based on innovative tools/lack of resources IT infrastructure General availability (e.g. internet)/lack of expertise/lack of resources Methodologies Unawareness of / discrepancy of national system with international methodologies/lack of training  

Smart systems: an enabling data resource? Smart Homes Smart Irrigation http://thesmarthousesolution.com/ Smart Grids http://www.smart-farms.net/

Smart system barriers and opportunities Literature is sparse on smart systems for national data Key opportunities: Major synergistic benefits with smart system deployment Unprecedented disaggregation and data matching, leading to new insights on energy system trends and opportunities Data collection efficiencies with proper upfront protocols Key challenges and barriers Lack of smart system data standards The “big data” problem Privacy and confidentiality Complexity and uncharted territory Cost and infrastructure

Priority research opportunities A roadmap for leveraging smart systems in national energy data systems Develop and propose a taxonomy of components, institutions, stakeholders, and information flows Identify opportunities and technical data characteristics for maximum synergies Identify major barriers (technical, economic, legal, behavioral, and policy) Propose priority actions, policies, and timelines for key system stakeholders Tailored advice for developed and developing economies

Priority research opportunities Technical assessment of smart metering and remote sensing technologies in the developing country context Systematic review of technical needs Need for predictive maintenance Low-cost options for data collection and reporting Need for interoperability with legacy equipment Assessment and design of standards for metering and sensing equipment in developing economies Engagement with major standards organizations (IEC, ISO) Match needs to a review of readiness in EEG countries of focus

Priority research opportunities Enable improved capacity for robust national data systems, particularly in developing countries Identify major technical and non-technical barriers limiting the development of data collection and use Identify and share good practices for data collection and use, with particular focus on energy demand Workshops, training, interviews Focus on opportunities for leveraging technologies Propose priority actions in specific country contexts Reports/white papers to synthesize outcomes for knowledge transfer

Thank you! And thanks to my IEA coauthors: Duncan Millard, Head, IEA Energy Data Centre Luis Munuera, Smart Grids Technology Lead Roberta Quadrelli, Head, Energy Balances, Prices, Emissions, Efficiency, IEA Energy Data Centre