LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
Problem
Traditional methods struggle to accurately forecast renewable energy demand when processing complex data, limiting sustainable planning.
Why it matters
Accurate renewable energy demand forecasts are described as key to sustainable business development amid severe global environmental change.
What the AI did
LTPNet integrates LSTM and Transformer deep learning with PSO parameter optimization within an environmental decision support system to forecast renewable energy demand.
Results
30% reduction in MAE, 20% decrease in MAPE, 25% drop in RMSE, 35% decline in MSE (comparison baseline not specified)
Impact
Claims substantial forecasting accuracy improvements; real-world adoption not stated in source.
Record
| Venue | Journal of Organizational and End User Computing |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Te Li, Mengze Zheng, Yan Zhou |
| Citations | 12 (OpenAlex, at import time) |
| References | 43 |
| Model or method | LTPNet (LSTM, Transformer, PSO-optimized) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.4018/joeuc.370005 |
| Open access | diamond |
| Keywords | renewable energy, demand forecasting, LSTM, Transformer, PSO, decision support |
Importance rating
real but narrower advance
Renewable energy demand forecasting framed as key for sustainable development amid global environmental change, but this is a method improvement without stated real-world scale.
More in Climate & Environment
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Learning skillful medium-range global weather forecasting | 2023 | 1,416 |
8
|
| 2 | Accurate medium-range global weather forecasting with 3D neural networks | 2023 | 1,631 |
8
|
| 3 | FuXi: a cascade machine learning forecasting system for 15-day global weather forecast | 2023 | 355 |
8
|
| 4 | Skilful nowcasting of extreme precipitation with NowcastNet | 2023 | 451 |
8
|
| 5 | Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects | 2023 | 378 |
8
|