SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON RF FEATURE EXTRACTION AND TCN-BiGRU MODEL

Liu Yili; Chen Yuanyuan

doi:10.19912/j.0254-0096.tynxb.2024-0440

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 682-689. DOI: 10.19912/j.0254-0096.tynxb.2024-0440

Special Topics of Academic Papers at the 100th Annual Meeting of the China Association for Science and Technology

SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON RF FEATURE EXTRACTION AND TCN-BiGRU MODEL

Liu Yili, Chen Yuanyuan

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Abstract

To solve the issues of input data redundancy and low prediction accuracy of single models in current photovoltaic power forecasting, a short-term PV power prediction model based on seasonal random forests （RF） feature extraction based on temporal convolutional network （TCN）, bidirectional gated recurrent unit network （BiGRU） and scaled-dot product attention mechanism （SDA） was constructed. Firstly, RF is employed to evaluate the contribution of each meteorological feature to power generation to select key features. Then, the key meteorological features and raw power data are imput into the TCN-BiGRU model combined with SDA mechanism. Finally, the proposed combination model is verified according to a practical example. The results demonstrate better prediction accuracy compared to other existing models.

Key words

feature extraction / random forest / neural networks / photovoltaic power generation forecast / SDA mechanism

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Liu Yili, Chen Yuanyuan. SHORT-TERM PHOTOVOLTAIC OUTPUT PREDICTION BASED ON RF FEATURE EXTRACTION AND TCN-BiGRU MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 682-689 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0440

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