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Evaluating Machine Learning for Predicting Next-Day Hot Water Production of a Heat Pump
RISE, Swedish ICT, SICS. Mälardalen University, Sweden.ORCID iD: 0000-0002-9890-4918
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an evaluation of five machine learning algorithms for predicting the domestic space and hot- water heating production for the next day. The evaluated algorithms were the k-nearest neighbour algorithm, linear regression, regression tree, decision table and support vector machine regression. The hot water production was measured in the ME3Gas project, where data was collected from two Swedish households that use the same type of geothermal heat pumps for space heating and hot-water production. The evaluation consisted of four experiments where we compared the regression performance by varying the number of previous days and the number of time periods for each day as input features. In the experiments, the k-nearest neighbour algorithm, linear regression and support vector machine regression had the best performance.

Place, publisher, year, edition, pages
2013, 7. p. 1688-1693, article id 6635871
Keywords [en]
Decision tables, Digital storage, Electrical engineering, Experiments, Heat pump systems, Learning algorithms, Regression analysis, Support vector machines, Water
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-24341DOI: 10.1109/PowerEng.2013.6635871Scopus ID: 2-s2.0-84887370878OAI: oai:DiVA.org:ri-24341DiVA, id: diva2:1043421
Conference
4th International Conference on Power Engineering, Energy and Electrical Drives
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2020-12-01Bibliographically approved

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