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Appl. Sci. 2017, 7(11), 1160; doi:10.3390/app7111160

Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing

1
Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University, Burgemeester Oudlaan 50, 3062PA Rotterdam, The Netherlands
2
Google Ireland Ltd, Google Docks, Barrow Street, D04 V4X7 Dublin, Ireland
3
Faculty of Management, Economics and Social Sciences, University of Cologne, Universitaetsstrasse 24, 50931 Cologne, Germany
4
Institute of Energy Economics, University of Cologne; Vogelsanger Str. 321a, 50827 Cologne, Germany
*
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 4 November 2017 / Accepted: 6 November 2017 / Published: 12 November 2017
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Abstract

Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems. View Full-Text
Keywords: dynamic pricing; customer segmentation; recommendation systems; demand response; demand side management; home energy management system; machine learning dynamic pricing; customer segmentation; recommendation systems; demand response; demand side management; home energy management system; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Koolen, D.; Sadat-Razavi, N.; Ketter, W. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing. Appl. Sci. 2017, 7, 1160.

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