University of Hertfordshire

By the same authors

Evaluation of Regression Algorithms and Features on the Energy Disaggregation Task

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publication10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728149592
DOIs
Publication statusPublished - Jul 2019
Event10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 - Patras, Greece
Duration: 15 Jul 201917 Jul 2019

Publication series

Name10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019

Conference

Conference10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
CountryGreece
CityPatras
Period15/07/1917/07/19

Abstract

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

ID: 19522072