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

Buildings

Mehrdad Arashpour¹  Haibo Feng²  Kasun Hewage³  Haonan Zhang³ 

[1] Department of Civil Engineering, Clayton Campus, Monash University, Melbourne, VIC 3800, Australia;Department of Mechanical and Construction Engineering, Northumbria University, Newcastle NE2 1XE, UK;School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, BC V1V1V7, Canada;

关键词: energy retrofits;    artificial neural network;    multi-objective optimization;    TOPSIS;   

DOI  :  10.3390/buildings12060829

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown