The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data.
The project will analyse in depth data from five homes that have been trialling the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project is to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project will deliver a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.
Bournemouth University and Baringa, working in partnership with ASI Data Science, will work independently of each other to provide information derived from the water, gas and electricity use in these UK homes, from the end of 2017 to middle of 2018.
