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.
|Publisher||IEEE Computer Society Press|
|Conference||19th International Conference on Systems Engineering, ICSENG '08|
|Period||19/08/08 → 21/08/08|
|Other||19-21 August 2008|