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WP1 Appliance Disaggregation: Dynamic Modelling

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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.

In this deliverable Bournemouth Uni presents an original approach for mining utility data usage patterns relying on a novel Deep Hierarchical Dynamic model which consists of three modules, a Deep Belief network (DBN), a hierarchical mixture model which is based on Latent Dirichlet Allocation (LDA) and a Dynamic Bayesian Network based on Hidden Markov Model (HMM), called DBN-LDA-HMM. This architecture aims at extracting topics from data while taking into account the temporal structure of the data to model the inter-topic sequential dependency. While the mathematical details of the proposed algorithm are described elsewhere, a full empirical evaluation of this pattern mining algorithm using the ETI data is discussed, highlighting its performance on various mining tasks.

Publication Year:

2018

Publisher:

ETI

Author(s):

Mohamad, S. and Bouchachia, H.

Language:

English

File Type:

application/pdf

File Size:

889690 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

Publication Type:

Technical Report

Subject:

Technology

Theme(s):

Smart Systems and Heat