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.

A predictive approach based on neural network models for building automation systems

De March, D.;Borrotti, M.;Sartore, Luca;Slanzi, D.;Poli, I.
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.
Advances in Neural Networks: Computational and Theoretical Issues
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3660248
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