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Energy Demand and Energy Networks
Energy Academy, School of Energy, Geosciences, Infrastructure and Society
9th September 2014
Dr David Jenkins and Dr Joel Chaney
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
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?
Energy Supply
Transmission/ Distribution
Energy Demand
Change of resource (e.g. wind/tidal/solar)
Ability of generation portfolio to react
Effect of climate shocks on system
Reduced heatingIncreased coolingNew technologiesChange in peak demand
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The effect of scenarios on demand...
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Future 1
Energy efficient lighting, e.g. LED ?
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Future 1
Future 2
Charge cycle of electric vehicles?
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Future 1
Future 2
Future 3
Continuing rise in consumer electronics?
Continuing rise in consumer electronics?00
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Climate Change?
APAtSCHE Project- UK EPSRC Project
Enabling the elderly to access energy innovation
Improved Occupancy sensing and Smart Thermostats
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.
Combine multiple low cost sensor hardware (examples only)
Use machine learning based time-sequence pattern recognition in order to classify activity detected.
Determine change in occupancy
Occupancy probability function
ORIGIN Project
Programmed setting by occupant
Occupancy probability function
Modified schedule
ON ONOFF OFF
ON OFFONOFF OFF
OFF
ORIGIN Project- EU FP7 Project
Orchestration of Renewable Integrated Generation In Neighbourhoods
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
Weather Prediction
Knapsack modelling approach
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Gen
erati
on/C
onsu
mpti
on (k
Wh
per 5
min
utes
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Consumption Generation
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Elec
tric
ity ta
riff
(p/k
Wh)
Time (h)
Available expertise
Understanding of energy demand and networks:Demand responseEffect of technology changeClimate changeOccupancy sensingUsing machine learning to identify patterns
in energy behaviour.Energy sensing and controlEnergy user interface design
