Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

Zhang, Haonan, Feng, Haibo, Hewage, Kasun and Arashpour, Mehrdad (2022) Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework. Buildings, 12 (6). p. 829. ISSN 2075-5309

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Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages.

Item Type: Article
Additional Information: Funding information: This research was funded by MITACS and FortisBC.
Uncontrolled Keywords: energy retrofits, artificial neural network, multi-objective optimization, TOPSIS
Subjects: G400 Computer Science
K200 Building
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 15 Jun 2022 09:10
Last Modified: 15 Jun 2022 09:15

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