Short-term Photovoltaic Power Forecasting Based on Improved Dung Beetle Optimizer for Optimizing VMD-BiLSTM

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In order to improve the accuracy of short-term photovoltaic power forecasting, a model integrating an im proved dung beetle optimizer, variational mode decomposition (VMD), and bidirectional long short-term memory (BiLSTM) was proposed. Firstly, a VMD-BiLSTM-based prediction framework was constructed, where time-series data were decomposed into multiple components via VMD and fed into BiLSTM for individual prediction. The final output was obtained by reconstructing the component-level results to enhance overall prediction performance. Sub sequently, to address the tendency of the dung beetle optimizer (DBO) to fall into local optima, an improved DBO algorithm (IDBO) was developed through the introduction of four strategies: logistic chaotic mapping for initializa tion, Levy flight for global exploration, golden sine strategy for position updating, and adaptive T-distribution per turbation for local exploitation. Finally, the IDBO was utilized to optimize critical parameters, including the decom position number K and penalty factor α in VMD, as well as the hidden layer size and Dropout ratio in BiLSTM, thereby enhancing the model′s learning capability and mitigating overfitting. The proposed model was experimental ly tested using actual data from photovoltaic power stations in Shandong and Hebei provinces. Compared to the un improved model DBO-VMD-BiLSTM, the results showed that the proposed model had the best MAE, MAPE and RMSE at two power stations.

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