Abstract
One of the way to save energy in smart buildings is the prediction of the variables that affect energy consumption. The aim of this paper is the application of fuzzy cognitive map for indoor temperature forecasting. Fuzzy cognitive map is a soft computing technique that describes the analyzed problem as a set of concepts and connections between them. The developed evolutionary algorithm for fuzzy cognitive maps learning is used to select the most significant concepts (sensors in a smart building) and determine the weights of the connections. The data captured in the SMLsystem created at the Universidad CEU Cardenal Herrera for participation in the Solar Decathlon 2013 competition were used in the experiments. Results show a high forecasting accuracy and they could be used to control smart building and to reduce the number of required sensors.
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Poczęta, K., Kubuś, Ł., Yastrebov, A., Papageorgiou, E.I. (2018). Temperature Forecasting for Energy Saving in Smart Buildings Based on Fuzzy Cognitive Map. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2018. AUTOMATION 2018. Advances in Intelligent Systems and Computing, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-77179-3_9
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