TY - JOUR
T1 - Artificial neural network for predicting building energy performance
T2 - a surrogate energy retrofits decision support framework
AU - Zhang, Haonan
AU - Feng, Haibo
AU - Hewage, Kasun
AU - Arashpour, Mehrdad
N1 - Funding Information:
Funding: This research was funded by MITACS and FortisBC.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - Assessing the energy performance of existing residential buildings (ERB) has been identi-fied 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 optimi-zation, 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 devel-oped 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.
AB - Assessing the energy performance of existing residential buildings (ERB) has been identi-fied 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 optimi-zation, 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 devel-oped 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.
KW - artificial neural network
KW - energy retrofits
KW - multi‐objective optimization
KW - TOPSIS
UR - http://www.scopus.com/inward/record.url?scp=85135233944&partnerID=8YFLogxK
U2 - 10.3390/buildings12060829
DO - 10.3390/buildings12060829
M3 - Article
AN - SCOPUS:85135233944
SN - 2075-5309
VL - 12
JO - Buildings
JF - Buildings
IS - 6
M1 - 829
ER -