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WP1 Appliance Disaggregation: Data Quality Report


Citation Favaro, A. and Zhihan Xu WP1 Appliance Disaggregation: Data Quality Report, ETI, 2017. https://doi.org/10.5286/UKERC.EDC.000591.
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Author(s) Favaro, A. and Zhihan Xu
Project partner(s) Baringa Partners LLP
Publisher ETI
DOI https://doi.org/10.5286/UKERC.EDC.000591
Download AdHoc_SSH_SS9019_1.pdf document type
Associated Project(s) ETI-SS1403: Smart Systems and Heat (SSH) Programme - Home Energy Management System (HEMS)
Associated Dataset(s) No associated datasets
Abstract The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

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 analysed in depth datafrom five homes that trialed 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 was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered 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.

The ETI collected utility meter and other data (e.g. room temperatures, humidity, and HEMS control data) from five dwellings over a period of six months. Using the collected data, work was conducted to evaluate different machine learning algorithms, research appropriate data features and calibrations thereof, and test the “art of the possible”. Thework sought not only to understand historical human activity within the building, but also to estimate probabilities of future hot water usage, occupancy and heating needs. This report presents the data quality issues present in the first 10 of 30 hard drives on which data from these homes was stored.