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Short-term Power Prediction of Distributed Photovoltaic Systems Based on Multi-scale Feature Fusion using TPE-CBiGRU
  • +4
  • Hongbo Zou,
  • Changhua Yang,
  • Hengrui MA,
  • Suxun Zhu,
  • Jialun Sun,
  • Jinlong Yang,
  • Jiahao Wang
Hongbo Zou
China Three Gorges University
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Changhua Yang
China Three Gorges University

Corresponding Author:[email protected]

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Hengrui MA
Qinghai University
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Suxun Zhu
Qinghai University
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Jialun Sun
Zhangjiakou Power Supply Company of State Grid Jibei Electric Power Co.
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Jinlong Yang
China Three Gorges University
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Jiahao Wang
Qinghai University
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Abstract

To address the key challenges of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and periodic characteristics of power in short-term power prediction of distributed photovoltaic (PV) stations, a TPE-CBiGRU model prediction method based on multi-scale feature fusion is proposed. Firstly, multi-scale feature fusion of meteorological features, temporal features, and hidden periodic features in PV power is performed to construct model input features. Secondly, CNN and Bi-GRU are utilized to model the feature relationships between PV power and its influencing factors from spatial and temporal scales, respectively, and the spatial-temporal features extracted are fused through an Add network. Finally, the Bayesian hyperparameter optimization method is adopted to further optimize network parameters, achieving the prediction of single-station PV power. Validation using measured data from a certain PV station shows that the proposed method enhances the comprehensiveness of feature information extraction from both data and model layers, significantly improving the accuracy of short-term PV power prediction. Compared with other prediction models, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by 26.11% and 35.64%, respectively, and the R-squared (R2) is increased by 3.07%.
Submitted to IET Generation, Transmission & Distribution
08 Jul 2024Review(s) Completed, Editorial Evaluation Pending
17 Jul 2024Editorial Decision: Revise Major
03 Aug 20241st Revision Received
05 Aug 2024Submission Checks Completed
05 Aug 2024Assigned to Editor
05 Aug 2024Review(s) Completed, Editorial Evaluation Pending
05 Aug 2024Reviewer(s) Assigned
19 Aug 2024Editorial Decision: Revise Minor
20 Aug 20242nd Revision Received
21 Aug 2024Submission Checks Completed
21 Aug 2024Assigned to Editor
21 Aug 2024Review(s) Completed, Editorial Evaluation Pending
21 Aug 2024Reviewer(s) Assigned
25 Aug 2024Editorial Decision: Accept