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.
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 Advances in Neural Networks: Computational and Theoretical Issues (pp.253-262). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-319-18164-6_24].
A predictive approach based on neural network models for building automation systems
Borrotti, Matteo;
2015
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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