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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
An Integrated Artificial Intelligence Approach for Building Energy Demand Forecasting
Author
Vieri, Andrea 1   VIAFID ORCID Logo  ; Gambarotta, Agostino 2   VIAFID ORCID Logo  ; Morini, Mirko 2   VIAFID ORCID Logo  ; Saletti, Costanza 2   VIAFID ORCID Logo 

 Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy; [email protected] (A.V.); [email protected] (A.G.); [email protected] (C.S.); Siram Veolia, Via Anna Maria Mozzoni, 12, 20152 Milan, Italy 
 Department of Engineering for Industrial Systems and Technologies, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy; [email protected] (A.V.); [email protected] (A.G.); [email protected] (C.S.); Center for Energy and Environment (CIDEA), University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy 
First page
4920
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
19961073
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3116643865
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.