Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 181, P. 106190 - 106190
Published: Aug. 24, 2024
Language: Английский
Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 181, P. 106190 - 106190
Published: Aug. 24, 2024
Language: Английский
Building and Environment, Journal Year: 2024, Volume and Issue: 250, P. 111176 - 111176
Published: Jan. 9, 2024
Language: Английский
Citations
31Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 140010 - 140010
Published: Dec. 2, 2023
Language: Английский
Citations
32Data, 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
14Environmental Impact Assessment Review, Journal Year: 2023, Volume and Issue: 103, P. 107232 - 107232
Published: Aug. 10, 2023
Language: Английский
Citations
21Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 346, P. 118962 - 118962
Published: Sept. 13, 2023
Language: Английский
Citations
20Sustainability, 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
19Sustainable Production and Consumption, Journal Year: 2024, Volume and Issue: 45, P. 316 - 332
Published: Jan. 4, 2024
Language: Английский
Citations
9Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 192, P. 110195 - 110195
Published: May 6, 2024
Language: Английский
Citations
9Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 439, P. 140879 - 140879
Published: Jan. 22, 2024
Language: Английский
Citations
8Ecological Modelling, Journal Year: 2024, Volume and Issue: 489, P. 110611 - 110611
Published: Jan. 4, 2024
Language: Английский
Citations
5