A Novel Approach for Predicting CO 2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model

Yuyi Hu (), Bojun Wang (), Yanping Yang and Liwei Yang
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Yuyi Hu: School of Civil and Architectural Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Bojun Wang: Zhangjiagang Campus, Jiangsu University of Science and Technology, Suzhou 215600, China
Yanping Yang: School of Civil and Architectural Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Liwei Yang: School of Energy and Power Engineering, Dalian University of Technology, Dalian 116000, China

Energies, 2024, vol. 17, issue 17, 1-17

Abstract: The accurate prediction of carbon dioxide (CO 2 ) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO 2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R 2 ) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO 2 emissions in the building industry, offering robust support for the sustainable development of the industry.

Keywords: carbon dioxide emission prediction; multi-strategy improved particle swarm algorithm; long short-term memory model; MSPSO-LSTM model; building industry (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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