A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics
Guardado en:
| Publicado en: | Applied Sciences vol. 15, no. 19 (2025), p. 10612-10626 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3261055098 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app151910612 |2 doi | |
| 035 | |a 3261055098 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Pan Hailong |u China State Grid Yichun Electric Power Supply Company, Yichun 336000, China; panhailong623@163.com (H.P.); lichao8501347@163.com (C.L.); 18807957837@163.com (F.X.) | |
| 245 | 1 | |a A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a High-precision photovoltaic (PV) power generation prediction models are essential for ensuring secure and stable grid operation and optimized dispatch. Existing models often ignore the significant variations in PV grid-connected inverter loss distributions and exhibit inadequate data decomposition processing, which influences the accuracy of the prediction models. This paper proposes a PSO-VMD-LSTM prediction model that includes PV converter loss characteristics. Firstly, the Particle Swarm Optimization (PSO) algorithm is employed to optimize the parameters of Variational Mode Decomposition (VMD), enabling effective decomposition of data under different weather conditions. Secondly, the decomposed sub-modes are individually fed into Long Short-Term Memory (LSTM) networks for prediction, and the results are subsequently reconstructed to obtain preliminary predictions. Finally, a neural network-based equivalent model for inverter losses is constructed; the preliminary predictions are fed into this model to obtain the final prediction results. Simulation case studies demonstrate that the proposed PSO-VMD-LSTM-based model can comprehensively consider the impact of uneven converter loss distribution and effectively improve the accuracy of PV power prediction models. | |
| 653 | |a Data processing | ||
| 653 | |a Accuracy | ||
| 653 | |a Random variables | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Forecasting | ||
| 653 | |a Bandwidths | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Signal processing | ||
| 653 | |a Statistical methods | ||
| 653 | |a Neural networks | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Li, Chao |u China State Grid Yichun Electric Power Supply Company, Yichun 336000, China; panhailong623@163.com (H.P.); lichao8501347@163.com (C.L.); 18807957837@163.com (F.X.) | |
| 700 | 1 | |a Xiao Fuming |u China State Grid Yichun Electric Power Supply Company, Yichun 336000, China; panhailong623@163.com (H.P.); lichao8501347@163.com (C.L.); 18807957837@163.com (F.X.) | |
| 700 | 1 | |a Zhou, Hai |u College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; zhubinxin@ctgu.edu.cn | |
| 700 | 1 | |a Zhu Binxin |u College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; zhubinxin@ctgu.edu.cn | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 19 (2025), p. 10612-10626 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261055098/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3261055098/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261055098/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |