RESEARCH ON POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC PLANTS BASED ON Q-LEARNING COMBINED MODEL

Tang Zhongjie; Kou Wenzhen; Yu Xue

doi:10.19912/j.0254-0096.tynxb.2024-1153

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 618-625. DOI: 10.19912/j.0254-0096.tynxb.2024-1153

RESEARCH ON POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC PLANTS BASED ON Q-LEARNING COMBINED MODEL

Tang Zhongjie¹, Kou Wenzhen², Yu Xue²

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Abstract

Aiming at the problem that the forecast of a single model in forecasting the generation power of photovoltaic power plants is not ideal due to its inherent limitations, and that the weight of the traditional combination model is constant and not optimal, and its forecast potential is not fully exploited, a prediction method based on the Q-learning combination model is proposed. Firstly, meteorological features and time series with strong correlation with power generation are selected as input features through Pearson correlation coefficient; Secondly, two single models namely XGBoost（eXtreme Gradient Boosting） and LSTM（Long Short Term Memory） are used to forecast the generation power; Finally, the Q-learning algorithm is used to perform online rolling optimization on historical samples and update the combination weights of each preliminary forecast in real-time, thereby achieving the optimal time-varying weight combination. The experimental results show that compared to other models, the method can significantly improve forecast accuracy and effectiveness, verifying the effectiveness of this method.

Key words

PV power generation / forecasting / XGBoost / LSTM / reinforcement learning / combined model

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Tang Zhongjie, Kou Wenzhen, Yu Xue. RESEARCH ON POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC PLANTS BASED ON Q-LEARNING COMBINED MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 618-625 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1153

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