An Integrated Artificial Intelligence Approach for Building Energy Demand Forecasting

Andrea Vieri, Agostino Gambarotta, Mirko Morini () and Costanza Saletti
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Andrea Vieri: Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Agostino Gambarotta: Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Mirko Morini: Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Costanza Saletti: Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy

Energies, 2024, vol. 17, issue 19, 1-28

Abstract: Buildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global greenhouse gas emissions. Consequently, substantial effort has been made to forecast their energy needs, with the scope of optimizing their economic and environmental impact. In this regard, the available literature focuses mainly on short-term modeling through the implementation of sets of physics-based equations (i.e., white-box), functional relationships between input and output variables (i.e., black-box), or a combination of both (i.e., grey-box). On the other hand, more research is required on long-term forecast models with the aim of reducing the energy needs. Within this context, this article presents an original automatic procedure for forecasting the energy needs of buildings in short- and long-term time horizons. This is accomplished by scaling an unknown facility from a similar facility that is already known and by executing a black-box approach based on machine learning algorithms. The proposed method is implemented in real case studies in Italy, predicting the energy needs (i.e., heating, cooling, and electricity) of Sant’Anna Hospital in Ferrara using the historical data of Ca’ Foncello Hospital in Treviso. The results show an adjusted coefficient of determination above 0.7 and an average error below 10% for all the energy vectors, demonstrating a feasible forecast performance with a low training set-to-test set ratio.

Keywords: long-term forecast; short-term forecast; machine learning; building energy needs; hyperparameter optimization; similitude approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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