Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135216 - 135216
Published: Feb. 1, 2025
Language: Английский
Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135216 - 135216
Published: Feb. 1, 2025
Language: Английский
Energy Engineering, Journal Year: 2025, Volume and Issue: 0(0), P. 1 - 10
Published: Jan. 1, 2025
Harnessing solar power is essential for addressing the dual challenges of global warming and depletion traditional energy sources.However, fluctuations intermittency photovoltaic (PV) pose its extensive incorporation into grids.Thus, enhancing precision PV prediction particularly important.Although existing studies have made progress in short-term prediction, issues persist, underutilization temporal features neglect correlations between satellite cloud images data.These factors hinder improvements performance.To overcome these challenges, this paper proposes a novel method based on multi-stage feature learning.First, improved LSTM SA-ConvLSTM are employed to extract spatial-temporal images, respectively.Subsequently, hybrid attention mechanism proposed identify interplay two modalities, capacity focus most relevant features.Finally, Transformer model applied further capture patterns long-term dependencies within multi-modal information.The also compares with various competitive methods.The experimental results demonstrate that outperforms methods terms accuracy reliability prediction.
Language: Английский
Citations
0Ocean Engineering, Journal Year: 2025, Volume and Issue: 320, P. 120317 - 120317
Published: Jan. 10, 2025
Language: Английский
Citations
0Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119501 - 119501
Published: Jan. 22, 2025
Language: Английский
Citations
0Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110188 - 110188
Published: Feb. 24, 2025
Language: Английский
Citations
0Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135216 - 135216
Published: Feb. 1, 2025
Language: Английский
Citations
0