Abstract
In this paper we address the problem of developing a control strategy to reduce the building energy consumption and reach indoor comfort levels. For this multiple and conflicting objectives optimisation we develop an approach based on stochastic feed-forward neural network models with ARIMA model predictions considered as input variables for networks. Studying real data from a sensorised office located in Rovereto (Italy) we develop the approach and achieve results exhibiting the very good performance of this predictive procedure.
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De March, D., Borrotti, M., Sartore, L., Slanz, D., Podestà, L., Poli, I. (2015). A Predictive Approach Based on Neural Network Models for Building Automation Systems. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_24
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DOI: https://doi.org/10.1007/978-3-319-18164-6_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18163-9
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