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Probabilistic Prediction of Photovoltaic Power Interval Based on Feature Mining and Improved TCN-BiGRU
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Yuan CAI1, 2, Hao WU1, 2, *, Dan TANG1, 2
Science Technology and Engineering | 2025, 25(10) : 4145 - 4155
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Science Technology and Engineering | 2025, 25(10): 4145-4155
• Papers·Electrical Technology •
Probabilistic Prediction of Photovoltaic Power Interval Based on Feature Mining and Improved TCN-BiGRU
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Yuan CAI1, 2, Hao WU1, 2, *, Dan TANG1, 2
Affiliations
  • 1 School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China
  • 2 Sichuan Key Laboratory of Artificial Intelligence, Yibin 644000, China
Published: 2025-04-08 doi: 10.12404/j.issn.1671-1815.2403394
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Photovoltaic power generation has an important place in the energy sector. In order to accurately quantify the uncertainty and fluctuation range of PV(photovoltaic) power and to improve the comprehensiveness of interval forecasts, a probabilistic prediction method for PV power intervals based on feature mining with improved TCN-BiGRU was proposed. First, the maximum information coefficient and symbolic transfer entropy causal analysis were utilized to screen the meteorological features, remove redundant information, and construct global horizontal radiation trend features, seasonal features, and weather clustering features to provide more effective information. Subsequently, the TCN-BiGRU model was improved by combining the temporal pattern attention mechanism and quantile regression methods to construct a combined model for interval prediction. Finally, the probabilistic prediction results are generated using the KDE method of empirical bandwidth selection with scatter measure semi-polar optimization. The proposed method is analyzed by real PV plant data, which verifies the high reliability and applicability of the proposed method in PV power interval probability prediction.

feature mining  /  TCN-BiGRU  /  quantile regression  /  kernel density estimation  /  interval probability prediction
Yuan CAI, Hao WU, Dan TANG. Probabilistic Prediction of Photovoltaic Power Interval Based on Feature Mining and Improved TCN-BiGRU[J]. Science Technology and Engineering, 2025 , 25 (10) : 4145 -4155 . DOI: 10.12404/j.issn.1671-1815.2403394
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Year 2025 volume 25 Issue 10
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Article Info
doi: 10.12404/j.issn.1671-1815.2403394
  • Receive Date：2024-05-08
  • Online Date：2025-07-09
  • Published：2025-04-08
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  • Received：2024-05-08
  • Revised：2025-01-10
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Affiliations
    1 School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China
    2 Sichuan Key Laboratory of Artificial Intelligence, Yibin 644000, China
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