A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation

• Published in: Journal of intelligent & fuzzy systems 2021-01, Vol.41 (6), p.7473-7484
• Main Authors: Chu, Xiaolin, Zhao, Ruijuan
• Format: Article
• Language: English
• Subjects: Algorithms; Carbon; Decision trees; Emission analysis; Multiple criterion; Noise intensity; Particle swarm optimization; Performance enhancement; Prediction models; Public health; Regression models; Robustness; Support vector machines
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-211435

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).