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.
Publication Year:
2017
Publisher:
ETI
Author(s):
Favaro, A. and Zhihan Xu
Energy Categories
Language:
English
File Type:
application/pdf
File Size:
2426025 B
Rights:
Energy Technologies Institute Open Licence for Materials
Rights Overview:
The Energy Technologies Institute is making this document available to use under the Energy Technologies Institute Open Licence for Materials. Please refer to the Energy Technologies Institute website for the terms and conditions of this licence. The Information is licensed "as is" and the Energy Technologies Institute excludes all representations, warranties, obligations and liabilities in relation to the Information to the maximum extent permitted by law. The Energy Technologies Institute is not liable for any errors or omissions in the Information and shall not be liable for any loss, injury or damage of any kind caused by its use. This exclusion of liability includes, but is not limited to, any direct, indirect, special, incidental, consequential, punitive, or exemplary damages in each case such as loss of revenue, data, anticipated profits, and lost business. The Energy Technologies Institute does not guarantee the continued supply of the Information. Notwithstanding any statement to the contrary contained on the face of this document, the Energy Technologies Institute confirms that it has the right to publish this document.
Further information:
N/A
Region:
United Kingdom
Related Dataset(s):
Home Energy Management System (HEMS) ICT Market Study - HEMS ICT Market Forecast Tool
Related Publications(s):
ETI Insights Report - Domestic Energy Services
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 - How can people get the heat they want at home without carbon ?
SSH Stagegate 1 - Review of International Smart Systems and Heat Initiatives - Final Report
WP1 Appliance Disaggregation: Data Analysis Pre-processing
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: Incorporation of Appliance and layout information
WP1 Appliance Disaggregation: Online learning and distributed learning