In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.

Indoor Air-Temperature Forecast for Energy-Efficient Management in Smart Buildings / Aliberti, Alessandro; Ugliotti, FRANCESCA MARIA; Bottaccioli, Lorenzo; Cirrincione, Giansalvo; Osello, Anna; Macii, Enrico; Patti, Edoardo; Acquaviva, Andrea. - (2018), pp. 1-6. ((Intervento presentato al convegno 18th IEEE International Conference on Environment and Electrical Engineering (EEEIC) tenutosi a Palermo, Italy nel 12-15 June 2018 [10.1109/EEEIC.2018.8494382].

Indoor Air-Temperature Forecast for Energy-Efficient Management in Smart Buildings

Alessandro Aliberti;Francesca Maria Ugliotti;Lorenzo Bottaccioli;Anna Osello;Enrico Macii;Edoardo Patti;Andrea Acquaviva
2018

Abstract

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2705244
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