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Short-term PV Power Prediction Model Based on Optimized TCN Combination Model
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Jun-hong LIU, Si-yuan FU, Ya-jun WANG*
Science Technology and Engineering | 2025, 25(15) : 6378 - 6388
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Science Technology and Engineering | 2025, 25(15): 6378-6388
Papers·Electrical Technology
Short-term PV Power Prediction Model Based on Optimized TCN Combination Model
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Jun-hong LIU, Si-yuan FU, Ya-jun WANG*
Affiliations
  • School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou 121000, China
Published: 2025-05-28 doi: 10.12404/j.issn.1671-1815.2404251
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To improve the short-term prediction accuracy of photovoltaic power generation models with multiple input features, a photovoltaic power prediction ensemble model LGGWO-TCN-MHSA based on optimizing TCN hyperparameters was proposed. The model integrated the levy gold grey wolf optimization (LGGWO), temporal convolutional network (TCN), and multi-head self-attention mechanism (MHSA). First, the Spearman correlation coefficient method extracted the main features that significantly affect photovoltaic power, which were then fed into the TCN prediction model. Then, the proposed multi-strategy LGGWO was applied to the TCN for hyperparameter optimization, which improved the model's prediction performance. Finally, the predicted values were input into the multi-head self-attention model to further boost prediction accuracy. The experiment was verified using original Australian photovoltaic data. By comparing with six groups of models including convolutional neural networks (CNN) and long short-term memory neural networks (LSTM), the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model on the test data set were reduced by 2.03%~82.0% and 10.5%~80.1%, respectively. The results show that the proposed method has high prediction accuracy and good stability.

photovoltaic power  /  PV power short-term forecast  /  improved grey wolf optimization  /  temporal convolutional network  /  multi-head self-attention
Jun-hong LIU, Si-yuan FU, Ya-jun WANG. Short-term PV Power Prediction Model Based on Optimized TCN Combination Model[J]. Science Technology and Engineering, 2025 , 25 (15) : 6378 -6388 . DOI: 10.12404/j.issn.1671-1815.2404251
Year 2025 volume 25 Issue 15
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doi: 10.12404/j.issn.1671-1815.2404251
  • Receive Date:2024-06-07
  • Online Date:2025-07-09
  • Published:2025-05-28
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  • Received:2024-06-07
  • Revised:2024-10-29
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    School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou 121000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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