As one of the representatives of renewable energy, solar energy is increasingly receiving widespread attention for its application in photovoltaic power generation. Solar power generation has many advantages, including cleanliness, environmental protection, and renewable characteristics. However, due to the intermittency and fluctuation of solar radiation, the stability and reliability of solar power generation systems face challenges. To better plan and manage electrical energy, accurately predicting solar radiation becomes a crucial task. Traditional time series forecasting methods have certain limits in processing large-scale datasets and improving forecasting results, so we turn to deep learning methods to obtain better performance. First, we selected LSTM as the baseline model, which has excellent performance in the field of time series prediction.