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WP1 Appliance Disaggregation: Pattern Mining

Citation Mohamad, S. Mansourim C. and Bouchachia, H. WP1 Appliance Disaggregation: Pattern Mining, ETI, 2018.
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Author(s) Mohamad, S. Mansourim C. and Bouchachia, H.
Project partner(s) Bournemouth University
Publisher ETI
Download AdHoc_SSH_SS9019_9.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 purpose of this deliverable is to describes the second task which is related to pattern mining. Specifically, it presents an original approach for mining utility data usage patterns relying on a novel algorithm, Gaussian Latent Dirichlet Allocation (GLDA). A full empirical evaluation of the proposed algorithm using the ETI data is discussed highlighting its performance on various mining tasks.