1. Making sense of household energy data
Gary Polhill, Tony Craig (The James Hutton Institute) and Pete Edwards (University of Aberdeen)

2. NESEMP
‘bridge’

3. Information from energy monitors
Conforms to standard required for smart meters
CurrentCost also provide a history graph for users with a ‘bridge’ to Cosm
How easy is it for householders to use this information to save energy?

4. Size and occupancy of houses important

5. Questionable effect of monitor
Introducing energy monitors in the home does not lead to long-term energy efficiency savings
Seasonal component to energy use
Important to back up data gathering with good social science (so we have the context of the energy usage)
Energy monitor installed here

6. Context-sensitivity Weather Wind, temperature, precipitation, sun
Locally affected by presence/absence of trees and buildings
Building fabric
Exposed surface area
Different materials have different thermal conductivities
Insulation, double-glazing, heating, thermostat, hot water
Room sizes
Demographics
Factors affecting thermal comfort
Use of the home
Occupancy
Lighting, gadgets, appliances, cooking
Personal hygiene practices
Regulation
Building regulations at time of construction

7. Collaboration with Dot.Rural
Provide people with an ‘energy forecast’ each day
Use the weather forecast and previous energy use and weather data
Also show how your forecast compares with others
‘Similar’ to you
Use responses to infrastructure and lifestyle questions to show how other users similar to you have saved energy in comparison with you
Being behind others (peers; those with similar living situation to you) is supposed to motivate you to investigate potential savings
Having a rich database linking household context to energy use would provide an evidence base giving users confidence to invest in infrastructure and energy-saving practices
Don’t just tell people how much energy they are using, but how their context affects it

8. Mock-up
Weather forecast (and diary?) used to predict user’s energy consumption
User’s current (or predicted) energy consumption can be compared with various groups
Differences between user and groups with biggest observed effect on consumption can be used to suggest actions
Your Friends
Others Like You
Turn Down Your
Thermostat 1C
Install LED Bulbs
-2.1
kWh
-0.5
kWh
33.5 kWh
Forecast for tomorrow
Find out more…

9. Collaboration with Dot.Rural
Connect a weather station and energy monitor to a Raspberry Pi
Upload data to a server
User can see their data (crude interface for now)
Questionnaire gathers data on infrastructure and demographics

10. Equipment installed in the home
Weather sensors as installed
Wind Speed & Direction
Weather sensors
Rainfall
Temp & Pressure
Raspberry Pi
Electricity Sensor
USB Hub
Weather Monitor
Electricity Monitor & Indoor Temp

11. Weather data source
Met Office Weather Observation Website

12. Issues Providing for gas readings No easy way to sense gas consumption
Huge variation in meters
But ideally would piggy-back off smart meter roll-out
No need to install equipment in the home (ideal)
Local climate? Might not need it…
Critical mass and curse of dimensionality
Each variable determining context adds exponentially to the sample size needed to provide users with meaningful comparisons
Methods for comparison may themselves be context-sensitive
Users may prefer to select populations to compare themselves with
Privacy

13. Summary
Presenting users with actionable information on their energy consumption requires awareness of their context and a meaningful population of others to compare themselves with
Also need some sort of trigger (e.g. energy forecast) to motivate them to investigate options
Gathering data from sensors should be backed up with socio-demographic and building fabric data
Technical aspects of data gathering are easy
Research needed on facilitating comparison and data analysis linking energy consumption to various factors (including weather)
Technical aspects of data interpretation are more challenging!