Domestic Heat Demand Prediction using Neural Networks

Vincent Bakker; Albert Molderink; Johann L. Hurink; Gerardus Johannes Maria Smit

doi:10.1109/ICSEng.2008.51

Domestic Heat Demand Prediction using Neural Networks

Vincent Bakker, Albert Molderink, Johann L. Hurink, Gerardus Johannes Maria Smit

Discrete Mathematics and Mathematical Programming

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

24 Citations (Scopus)

97 Downloads (Pure)

Abstract

By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine the electricity production capacity of the large ﬂeet of microCHP appliances. All predictions are short-term for one day) and use historical heat demand and weather inﬂuences as input.

Original language	Undefined
Title of host publication	Proceedings of Nineteenth International Conference on Systems Engineering
Place of Publication	Los Alamitos
Publisher	IEEE Computer Society
Pages	189-194
Number of pages	6
ISBN (Print)	978-0-7695-3331-5
DOIs	https://doi.org/10.1109/ICSEng.2008.51
Publication status	Published - 19 Aug 2008
Event	19th International Conference on Systems Engineering, ICSENG '08 - Las Vegas, Nevada, USA Duration: 19 Aug 2008 → 21 Aug 2008

Publication series

Name
Publisher	IEEE Computer Society Press
Number	Supplement

Conference

Conference	19th International Conference on Systems Engineering, ICSENG '08
Period	19/08/08 → 21/08/08
Other	19-21 August 2008

Keywords

EWI-13435
METIS-251181
IR-64981

Access to Document

10.1109/ICSEng.2008.51

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Cite this

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title = "Domestic Heat Demand Prediction using Neural Networks",

abstract = "By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine the electricity production capacity of the large ﬂeet of microCHP appliances. All predictions are short-term for one day) and use historical heat demand and weather inﬂuences as input.",

keywords = "EWI-13435, METIS-251181, IR-64981",

author = "Vincent Bakker and Albert Molderink and Hurink, {Johann L.} and Smit, {Gerardus Johannes Maria}",

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booktitle = "Proceedings of Nineteenth International Conference on Systems Engineering",

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Domestic Heat Demand Prediction using Neural Networks. / Bakker, Vincent; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria.

Proceedings of Nineteenth International Conference on Systems Engineering. Los Alamitos : IEEE Computer Society, 2008. p. 189-194.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Domestic Heat Demand Prediction using Neural Networks

AU - Bakker, Vincent

AU - Molderink, Albert

AU - Hurink, Johann L.

AU - Smit, Gerardus Johannes Maria

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PY - 2008/8/19

Y1 - 2008/8/19

N2 - By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine the electricity production capacity of the large ﬂeet of microCHP appliances. All predictions are short-term for one day) and use historical heat demand and weather inﬂuences as input.

AB - By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine the electricity production capacity of the large ﬂeet of microCHP appliances. All predictions are short-term for one day) and use historical heat demand and weather inﬂuences as input.

KW - EWI-13435

KW - METIS-251181

KW - IR-64981

U2 - 10.1109/ICSEng.2008.51

DO - 10.1109/ICSEng.2008.51

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BT - Proceedings of Nineteenth International Conference on Systems Engineering

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