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.
paper
Building automation system (BAS); Energy efficiency; Feed-forward neural networks; Prediction; Time series models;
English
Italian Workshop on Neural Network
2014
Advances in Neural Networks: Computational and Theoretical Issues
978-3-319-18163-9
2015
37
253
262
none
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214672
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