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Energy Demand and Energy Networks

Energy Demand and Energy Networks. Dr David Jenkins and Dr Joel Chaney. Energy Academy, School of Energ y, Geosciences , Infrastructure and Society 9th September 2014. Urban Energy Research Group. Active since 2004 Multi-disciplinary group Core research topics of:

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Energy Demand and Energy Networks

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  1. Energy Demand and Energy Networks Dr David Jenkins and Dr Joel Chaney Energy Academy,School of Energy, Geosciences, Infrastructure and Society 9th September 2014

  2. Urban Energy Research Group • Active since 2004 • Multi-disciplinary group • Core research topics of: • Energy demand data profiling • Adaptation to future climates • Energy systems and networks • Building performance simulation/modelling • Fuel poverty • Life-cycle carbon analysis @HWUrbanEnergy

  3. ARIES Project • Adaptation and Resilience In Energy Systems • University of Edinburgh (supply-side) and Heriot-Watt University (demand-side) • Modelling the effect of climate and future conditions on energy demand, supply and infrastructure • What problems might occur that are caused or exacerbated by climate change?

  4. Change of resource (e.g. wind/tidal/solar) Ability of generation portfolio to react Energy Supply Transmission/ Distribution Effect of climate shocks on system Reduced heating Increased cooling New technologies Change in peak demand Energy Demand

  5. The effect of scenarios on demand...

  6. Energy efficient lighting, e.g. LED ?

  7. Charge cycle of electric vehicles?

  8. Continuing rise in consumer electronics?

  9. Continuing rise in consumer electronics? Climate Change?

  10. APAtSCHE Project- UK EPSRC Project Enabling the elderly to access energy innovation

  11. Improved Occupancy sensing and Smart Thermostats Use machine learning based time-sequence pattern recognition in order to classify activity detected. Combine multiple low cost sensor hardware (examples only) Determine change in occupancy Occupancy probability function If you can predict when people are in the house you can dynamically tune their programmable thermostat setting for them as the season and their habits and schedules change.

  12. ORIGIN Project Programmed setting by occupant OFF Occupancy probability function Modified schedule OFF OFF ON ON OFF OFF ON OFF ON

  13. ORIGIN Project- EU FP7 Project Orchestration of Renewable Integrated Generation In Neighbourhoods

  14. Weather Prediction • Forecast and observation data for c37 sites • Capture local data and from weather direction • Every hour predict next 24 hours weather at hourly precision • Multiple linear regression

  15. Weather Prediction

  16. Knapsack modelling approach

  17. Available expertise • Understanding of energy demand and networks: • Demand response • Effect of technology change • Climate change • Occupancy sensing • Using machine learning to identify patterns in energy behaviour. • Energy sensing and control • Energy user interface design

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