An improved hybrid neural network algorithm for predicting photovoltaic output power: Considering the seasonal output characteristics of solar energy

The precise forecasting of photovoltaic energy generation holds paramount importance in refining scheduling and ensuring safe operation of extensive photovoltaic power stations. However, the inherent instability and volatility of photovoltaic power generation pose significant challenges to prediction accuracy. To address this, this article conducts a thorough analysis of the seasonal characteristics of photovoltaic power generation and introduces a hybrid prediction model based on the ensemble empirical mode decomposition (EEMD)-improved whale optimization algorithm (IWOA)-bidirectional long short-term memory network (BiLSTM) algorithm. This model leverages multi-seasonal meteorological features to enhance forecasting accuracy. First, EEMD is used to decompose and reconstruct photovoltaic power generation data to eliminate its instability and volatility. Second, three improved strategies are proposed for the position update in different stages of the IWOA, and a multi-seasonal prediction model based on IWOA-optimized Bidirectional LSTM is established. Finally, the operational data of a photovoltaic power station in the northwest region of China are used as a case study to evaluate the prediction performance of the model in detail. The results show that the model's accuracy rate ranges from 97.1% to 98.7%, which can accurately predict photovoltaic power generation and improve the utilization rate of renewable energy.