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Evaluating Machine Learning for Predicting Next-Day Hot Water Production of a Heat Pump

Authors:

Tomas Olsson

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

4th International Conference on Power Engineering, Energy and Electrical Drives

DOI:

10.1109/PowerEng.2013.6635871

Abstract

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 regres- sion. 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.

Bibtex

@inproceedings{Olsson3024,
author = {Tomas Olsson},
title = {Evaluating Machine Learning for Predicting Next-Day Hot Water Production of a Heat Pump},
pages = {1688--1693},
month = {May},
year = {2013},
booktitle = {4th International Conference on Power Engineering, Energy and Electrical Drives },
url = {http://www.es.mdu.se/publications/3024-}
}