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WP1 Appliance Disaggregation: Incorporation of Appliance and layout information


Citation Jamil, W. and Bouchachia, H. WP1 Appliance Disaggregation: Incorporation of Appliance and layout information, ETI, 2018. https://doi.org/10.5286/UKERC.EDC.000592.
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Author(s) Jamil, W. and Bouchachia, H.
Project partner(s) Bournemouth University
Publisher ETI
DOI https://doi.org/10.5286/UKERC.EDC.000592
Download AdHoc_SSH_SS9019_6.pdf document type
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 deliverable addresses the problem of prediction of hot water usage and gas using online regression models. While there was no access to the quantity of hot water consumed over time by the inhabitant, the inference can be made using the temperature of the domestic hot water flow and the central heating flow. The algorithmic steps are provided in this report, and a full description of the algorithms is given in the referred papers
Associated Project(s) ETI-SS1403: Smart Systems and Heat (SSH) Programme - Home Energy Management System (HEMS)
Associated Dataset(s) No associated datasets
Associated Publication(s)

ETI Insights Report - Domestic Energy Services

Home Energy Management System (HEMS) ICT Market Study - HEMS ICT Market Forecast Tool

Home Energy Management System (HEMS) ICT Market Study - Main Report

Home Energy Management System (HEMS) ICT Market Study - Market Forecast Supplemental Report

Home Energy Management System (HEMS) ICT Market Study - Request for proposals

Infographic - Domestic Heat

Infographic - How can people get the heat they want at home without carbon ?

List of projects for the ETI Smart Systems and Heat (SSH) Programme

SSH Stagegate 1 - Review of International Smart Systems and Heat Initiatives - Final Report

WP1 Appliance Disaggregation: Data Analysis Pre-processing

WP1 Appliance Disaggregation: Data Quality Report

WP1 Appliance Disaggregation: Dynamic Modelling

WP1 Appliance Disaggregation: Final Report

WP1 Appliance Disaggregation: High Frequency Appliance Disaggregation Analysis Handover

WP1 Appliance Disaggregation: High Frequency Appliance Disaggregation Analysis: Insights Overview

WP1 Appliance Disaggregation: Online learning and distributed learning

WP1 Appliance Disaggregation: Pattern Mining