Sensors Ralph Meijers
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This document discusses measuring the energy transition through increased use of sensor data. It notes the need for more regional data on renewable energy potential, supply and demand to inform policy. Sensors can provide data on energy consumption, grid demand, solar irradiance and aerial images. This data can help create smarter grids with higher efficiency and stability by better balancing supply and demand. It also discusses how different parties have interests in both short-term balancing and long-term infrastructure planning. The document explores using sensor data like smart meters and satellite imagery to estimate solar power production and monitor the energy transition's effects on areas like CO2 emissions and economic activity.
![Preliminary results (yearly pattern)
9
(disclaimer: NOT fit-for-use yet)
power from grid (known)
solar power (estimated)
Energydemand[MWh]
2010 2011 2012 2013 2014 2015 2016
Year](https://image.slidesharecdn.com/sensorsralphmeijers-171003122753/85/Sensors-Ralph-Meijers-9-320.jpg)
![Energy savings (solar panels)
17
w. solar panelsAll buildings
#households
#households
-10.000 -5.000 0 5.000-10.000 -5.000 0 5.000
difference [kWh] difference [kWh]
- Compare energy consumption year t+1 vs t-1
- Only consider ‘constant’ households
- Solar panels installed in year t (if present)
2014](https://image.slidesharecdn.com/sensorsralphmeijers-171003122753/85/Sensors-Ralph-Meijers-17-320.jpg)
Sensors Ralph Meijers
- 2. Measuring the energy transition Ralph Meijers smartflowerTM
- 3. Sensors for smart statistics - Energy transition: - Potential, supply & demand of renewable energy - Need for more regional data - Policy urgency - Sensors: - Gas & electricity consumption (smart meters) - Public grid demand (Tennet) - Solar irradiance at Earth’s surface (KNMI) - Aerial / satellite images (NSO / ESA) 3
- 4. The energy transition 4 Future? Smart grids: - Higher efficiency - Higher stability Balancing supply & demand Data / insight (regional & high frequency)
- 5. Parties & interests 5 Net operators / energy companies Short term: balancing supply & demand Long term: infrastructure Policy makers Climate goals Influence behaviour Competitiveness local economy House owners Sustainable investments
- 6. Parties & interests 6 Net operators / energy companies Short term: balancing supply & demand Long term: infrastructure Policy makers Climate goals Influence behaviour Competitiveness local economy House owners Sustainable investments ? - Where to start? - Best “Return on Investment”?
- 7. Important piece of the puzzle: solar energy Renewable energy - Wind, solar, … - Weather & season dependent volatility - Gasless neighbourhoods? User behaviour Data source: Smart meters … ? 7
- 8. Inferring solar power from grid demand 8 Jan 2016 Public grid demand in MWh (from Tennet) Solar irradiance in J/m2 (from KNMI) Jul 2016 Dec 2016 Jan 2016 Jul 2016 Dec 2016
- 9. Preliminary results (yearly pattern) 9 (disclaimer: NOT fit-for-use yet) power from grid (known) solar power (estimated) Energydemand[MWh] 2010 2011 2012 2013 2014 2015 2016 Year
- 10. A different approach Model solar energy production: − location and power of solar panels − orientation of roof − solar irradiance 10
- 11. Location of solar panels Several registrations: − Net operators (PIR) − Energylabel database Incomplete and not collected for statistical purposes Add tax data − Incentive: tax return 11
- 12. Solar panels in the Netherlands 12
- 13. Solar panels in South of Limburg 13
- 14. Solar panels in South of Limburg 14 Policy works − In some areas faster growth of number of solar panels. − Influence of policy? E.g. “Solar panel project” Landgraaf / Parkstad
- 15. Extract solar panel locations from aerial images 15
- 16. First step: extract buildings Machine visionAdmin data ‘positive’ ‘negative’ Extract solar panel locations from aerial images 16
- 17. Energy savings (solar panels) 17 w. solar panelsAll buildings #households #households -10.000 -5.000 0 5.000-10.000 -5.000 0 5.000 difference [kWh] difference [kWh] - Compare energy consumption year t+1 vs t-1 - Only consider ‘constant’ households - Solar panels installed in year t (if present) 2014
- 18. Energy savings potential (private properties) 18 Source: Energiebesparingspotentie koopwoningen ([email protected]) theoreticaltheoretical realisticrealistic Work in progress: potential solar panels
- 19. Further plans − City portals / local energy balance − Monitor CO2 emissions − Energy transition economic activity 19
- 20. Thanks to: − Bart Buelens − Tim de Jong − Olga Skriabikova − Jacqueline van Beuningen − Jos Erkens & Hans Muller − Alex Priem − Martijn Tennekes − Marijn Zuurmond − Otto Swertz − Anne Miek Kremer − Jurriën Vroom − Reinoud Segers − Olav ten Bosch − Sofie De Broe 20