A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model DOI Creative Commons

Yuyi Hu,

Bojun Wang, Yanping Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(17), P. 4379 - 4379

Published: Sept. 1, 2024

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model predicting CO2 that combines multi-strategy improved particle swarm optimization (MSPSO) algorithm with long short-term memory (LSTM) model. Firstly, (PSO) is enhanced by combining tent chaotic mapping, mutation least-fit particles, random perturbation strategy. Subsequently, performance MSPSO evaluated using set 23 internationally recognized test functions. Finally, predictive MSPSO-LSTM assessed from Yangtze River Delta region as case study. results indicate coefficient determination (R2) reaches 0.9677, which more than 10% higher BP, LSTM, CNN non-hybrid models demonstrates significant advantages over PSO-LSTM, GWO-LSTM, WOA-LSTM models. Additionally, mean square error (MSE) 2445.6866 Mt, absolute (MAE) 4.1010 both significantly lower those Overall, high accuracy industry, offering robust development industry.

Language: Английский

Assessment of operational carbon emissions for residential buildings comparing different machine learning approaches: A study of 34 cities in China DOI

Rongming Huang,

Xiaocun Zhang, Kaihua Liu

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 250, P. 111176 - 111176

Published: Jan. 9, 2024

Language: Английский

Citations

26

Industrial carbon emission forecasting considering external factors based on linear and machine learning models DOI
Ye Liang, Pei Du, Shubin Wang

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 140010 - 140010

Published: Dec. 2, 2023

Language: Английский

Citations

30

Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review DOI Creative Commons
Manuel Jaramillo, Wilson Pavón, L.F. Jaramillo

et al.

Data, Journal Year: 2024, Volume and Issue: 9(1), P. 13 - 13

Published: Jan. 11, 2024

This paper addresses the challenges in forecasting electrical energy current era of renewable integration. It reviews advanced adaptive methodologies while also analyzing evolution research this field through bibliometric analysis. The review highlights key contributions and limitations models with an emphasis on traditional methods. analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, deep learning have potential to model dynamic nature consumption, but they higher computational demands data requirements. aims offer a balanced view advancements methods, guiding researchers, policymakers, industry experts. advocates for collaborative innovation enhance accuracy support development resilient, sustainable systems.

Language: Английский

Citations

14

Analysis of spatial and temporal carbon emission efficiency in Yangtze River Delta city cluster — Based on nighttime lighting data and machine learning DOI
Qingqing Sun, Hong Chen, Yujie Wang

et al.

Environmental Impact Assessment Review, Journal Year: 2023, Volume and Issue: 103, P. 107232 - 107232

Published: Aug. 10, 2023

Language: Английский

Citations

19

Predicting carbon futures prices based on a new hybrid machine learning: Comparative study of carbon prices in different periods DOI
Xi Zhang, Kailing Yang, Qin Lu

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 346, P. 118962 - 118962

Published: Sept. 13, 2023

Language: Английский

Citations

19

Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces DOI Open Access
Zhonghua Han,

Bingwei Cui,

Liwen Xu

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13934 - 13934

Published: Sept. 20, 2023

Global warming is a major environmental issue facing humanity, and the resulting climate change has severely affected environment daily lives of people. China attaches great importance to actively responds issues. In order achieve “dual carbon” goal, it necessary clearly define emission reduction path scientifically predict future carbon emissions, which basis for setting targets. To ensure accuracy data, this study applies coefficient method calculate emissions from energy consumption in 30 provinces, regions, cities 1997 2021. Considering spatial correlation between different regions China, we propose new machine learning prediction model that incorporates weighting, namely, an LSTM-CNN combination with weighting. The weighting explains combined used analyze 2022 2035 under scenarios. results show four convolutional layers performs best. Compared other models, best predictive performance, MAE 8.0169, RMSE 11.1505, R2 0.9661 on test set. Based scenario predictions, found most can peaking before 2030. Some need adjust their development rates based specific circumstances as early possible. This provides research direction deep time series forecasting proposes forecasting.

Language: Английский

Citations

18

A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement DOI
Long-Hao Yang, Fei-Fei Ye, Haibo Hu

et al.

Sustainable Production and Consumption, Journal Year: 2024, Volume and Issue: 45, P. 316 - 332

Published: Jan. 4, 2024

Language: Английский

Citations

9

How can the Pearl River Delta urban agglomeration achieve the carbon peak target: Based on the perspective of an optimal stable economic growth path DOI

Yanchun Rao,

Xiuli Wang, Hengkai Li

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 439, P. 140879 - 140879

Published: Jan. 22, 2024

Language: Английский

Citations

8

An innovative data-feature-driven approach for CO2 emission predictive analytics: A perspective from seasonality and nonlinearity characteristics DOI
Song Ding, Xingao Shen,

Huahan Zhang

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 192, P. 110195 - 110195

Published: May 6, 2024

Language: Английский

Citations

8

Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting DOI
Kailing Yang, Xi Zhang, Haojia Luo

et al.

Energy, Journal Year: 2024, Volume and Issue: 298, P. 131321 - 131321

Published: April 16, 2024

Language: Английский

Citations

5