DocumentCode :
2731896
Title :
Domestic Heat Demand Prediction Using Neural Networks
Author :
Bakker, Vincent ; Molderink, Albert ; Hurink, Johann L. ; Smit, Gerard J M
Author_Institution :
Dept. of EEMCS, Twente Univ., Enschede
fYear :
2008
fDate :
19-21 Aug. 2008
Firstpage :
189
Lastpage :
194
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 :
load forecasting; neural nets; power engineering computing; power plants; domestic heat demand prediction; electricity production capacity; historical heat demand; microCHP appliances; neural networks; virtual power plant; weather influences; Cogeneration; Energy consumption; Home appliances; Neural networks; Power generation; Production; Resistance heating; Solar heating; Solar power generation; Water heating; distributed generation; neural networks; short-term forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems Engineering, 2008. ICSENG '08. 19th International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-0-7695-3331-5
Type :
conf
DOI :
10.1109/ICSEng.2008.51
Filename :
4616635
Link To Document :
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