How can internal temperature data be used to determine whether households are zoning?
Zareen Sethna, UCL Energy Institute
Heating controls have the potential to reduce household energy demand, but in order to calculate realistic estimates of their energy saving potential it is necessary to understand occupants’ current heating behaviours.
Zoning is the behaviour where occupants deliberately heat some rooms more or less than others, or at different times. This dissertation investigates how internal temperature data can be used to determine whether households are using thermostatic radiator valves (TRVs) to zone.
Within case study experiments were conducted to investigate the effect of different zoning behaviours on temperature data.
This data was then analysed and used to develop an algorithm that detects whether households are zoning.
To develop an algorithm that determines whether households are using TRVs to zone.
- Design and implement research that gathers temperature data akin to that in existing data sets but under various specified zoning regimes.
- Analyse a subset of the temperature data to establish whether zoning generates a recognisable signature distinct from other sources of temperature variation.
- Develop an indicator to discriminate between zoning and not zoning in the temperature data.
- Develop an algorithm that detects this indicator.
- Check the validity of the indicator and algorithm using the remaining data.
The algorithm has been shown to successfully detect one form of zoning behaviour, whether the radiator is on (TRV set to above 1) in one room and off (TRV set to 1 or below) in another, within the case study dwellings. Further work is needed to validate the indicator before it can be used to detect zoning in other temperature data sets.
The indicator that has been developed is a first, exploratory step into operationalizing the concept of zoning and determining the zoning behaviours of households. Despite the limited scope of the indicator, it still represents an extension to the methods currently used to infer heating practices from temperature data.