MARC details
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02438nam a2200277Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251009160712.0 |
| 040 ## - CATALOGING SOURCE |
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CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
B-21984 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251009s9999 xx 000 0 und d |
| 245 10 - TITLE STATEMENT |
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| Title |
Short-Term Photovoltaic Power Prediction Based on CEEMDAN and Hybrid Neural Networks. |
| 490 0# - SERIES STATEMENT |
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| Series statement |
IEEE Journal of Photovoltaics, 14(6), p.960-969, 2024 |
| 500 ## - GENERAL NOTE |
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| General note |
Artículo |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Accurate 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 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
COMPLETE ENSEMBLE EMPIRICAL MODE DECOMPOSITION WITH ADAPTIVE NOISE (CEEMDAN) |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
CONVOLUTIONAL NEURAL NETWORK (CNN) |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
GATE RECURRENT UNIT (GRU) |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
HYBRID NEURAL NETWORKS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
PHOTOVOLTAIC (PV) POWER PREDICTION |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Wu, S. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Guo, H. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Zhang, X. |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Wang, F. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1MLTbZrc62kUjTymja2ayVhP7alW8BR_-/view?usp=drive_link">https://drive.google.com/file/d/1MLTbZrc62kUjTymja2ayVhP7alW8BR_-/view?usp=drive_link</a> |
| Public note |
Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Clasificación local |
| Koha item type |
Documentos solicitados |
