Short Term Power Prediction Method for BOHB Elman Photovoltaic Power Stations Based on Similar Days
With the increasing proportion of renewable energy in the global energy structure,power prediction of photovoltaic power plants has become an important link in grid management and energy scheduling.This study proposes a short-term photovoltaic power prediction method that combines BOHB optimization algorithm and Elman neural network.The method focuses on the selection of similar days in historical data to improve prediction accuracy.The study utilized advanced data processing techniques to conduct in-depth analysis of a large amount of historical data,selecting the days that are most similar to the predicted daily meteorological and power generation characteristics,and then using these data to train the BOHB Elman model.The experimental results show that this method has higher accuracy and stability compared to traditional prediction models,providing a new solution for energy management of photovoltaic power plants.
photovoltaic power predictionElman neural networkBOHB algorithmsimilar day
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