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

• Published in: Buildings (Basel) Vol. 12; no. 6; p. 829
• Main Authors: Zhang, Haonan, Feng, Haibo, Hewage, Kasun, Arashpour, Mehrdad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Algorithms; artificial neural network; Artificial neural networks; Big Data; Carbon; Case studies; Costs; Decision making; Economic impact; Economic models; Economics; Emissions; Energy conservation; Energy consumption; Energy efficiency; energy retrofits; Genetic algorithms; Green buildings; Greenhouse gases; Impact analysis; Machine learning; multi-objective optimization; Multiple criterion; Multiple objective analysis; Neural networks; Optimization; Packages; Pareto optimization; Performance evaluation; Performance prediction; Residential areas; Residential buildings; Residential energy; Retrofitting; Simulation; TOPSIS; Canada; United States > US
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings12060829

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.