| 000 | 02438nam a2200277Ia 4500 | ||
|---|---|---|---|
| 003 | MX-MdCICY | ||
| 005 | 20251009160712.0 | ||
| 040 | _cCICY | ||
| 090 | _aB-21984 | ||
| 245 | 1 | 0 | _aShort-Term Photovoltaic Power Prediction Based on CEEMDAN and Hybrid Neural Networks. |
| 490 | 0 | _aIEEE Journal of Photovoltaics, 14(6), p.960-969, 2024 | |
| 500 | _aArtÃculo | ||
| 520 | 3 | _aAccurate photovoltaic (PV) power prediction technology plays a crucial role in effectively addressing the challenges posed by the integration of large-scale PV systems into the grid. In this article, we propose a novel PV power combination prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in conjunction with a hybrid neural network. To mitigate the influence of strong fluctuations in PV power on prediction outcomes, we employ CEEMDAN to decompose the PV data into several subsequences. Subsequently, sample entropy (SE) is used to quantify the complexity of each subsequence. Subsequences with similar SE values are then restructured to reduce computational load. Moreover, to overcome the limitations of a single neural network in capturing historical data features of PV power, a hybrid sequential convolutional neural network-gate recurrent unit (CNN-GRU) neural network is proposed. The effectiveness of our proposed model is validated through case studies involving PV power stations in two regions. To provide a comprehensive assessment, we conduct comparative validation by building and evaluating alternative models, including long-short term memory (LSTM), GRU, CEEMDAN-LSTM, CEEMDAN-GRU, and CNN-GRU. The results unequivocally demonstrate that the model presented in this article exhibits exceptional prediction performance, characterized by high accuracy and robust generalization. | |
| 650 | 1 | 4 | _aCOMPLETE ENSEMBLE EMPIRICAL MODE DECOMPOSITION WITH ADAPTIVE NOISE (CEEMDAN) |
| 650 | 1 | 4 | _aCONVOLUTIONAL NEURAL NETWORK (CNN) |
| 650 | 1 | 4 | _aGATE RECURRENT UNIT (GRU) |
| 650 | 1 | 4 | _aHYBRID NEURAL NETWORKS |
| 650 | 1 | 4 | _aPHOTOVOLTAIC (PV) POWER PREDICTION |
| 700 | 1 | 2 | _aWu, S. |
| 700 | 1 | 2 | _aGuo, H. |
| 700 | 1 | 2 | _aZhang, X. |
| 700 | 1 | 2 | _aWang, F. |
| 856 | 4 | 0 |
_uhttps://drive.google.com/file/d/1MLTbZrc62kUjTymja2ayVhP7alW8BR_-/view?usp=drive_link _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 |
_2Loc _cREF1 |
||
| 008 | 251009s9999 xx 000 0 und d | ||
| 999 |
_c62072 _d62072 |
||