Short-term Photovoltaic Power Prediction Method based on ISABO-DELM
To address the issue of inaccurate precision due to the randomness and instability of photovoltaic power generation, this paper proposes a short-term photovoltaic power prediction model based on an Improved Subtraction Average Based Optimizer (ISABO) and Deep Extreme Learning Machine (DELM). First, the k-means algorithm is used to classify weather types, and then Variational Mode Decomposition (VMD) is employed to decompose the original signal, fully extracting input factor information from the dataset to improve data quality. Next, an improved ISABO algorithm is proposed to optimize the parameters of the input layer and thresholds of the DELM model. Tent chaotic reverse learning is utilized to initialize the SABO population, enhancing population quality. The ISABO algorithm incorporates a Lévy flight mutation strategy to avoid local optima. Finally, the predicted values of different sequences are superimposed to obtain the final prediction result. Simulation results indicate that the proposed ISABO-DELM model has a smaller prediction error.
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