Deep Learning Application for Solar Power Generation Forecasting with Weather Patterns

This paper aims to forecast short-term solar power generation one hour ahead to enable precise power dispatching. Typically, time-series generation data is input into deep learning networks such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Neural Networks (DNN) for training. In this study, weather features are preprocessed and input into deep learning models alongside the generation data. First, the historical data's weather features and their correlation with future power generation are analyzed to select seven key features: power generation, temperature, relative humidity, sunshine duration, global solar radiation, UV index, and cloud amount. Subsequently, temperature, relative humidity, and UV index values are clustered into seven categories using k-means clustering. The average solar power generation is calculated based on forecasted weather features, and several neural networks are established for different ranges of power generation for training and testing. The research results show that using the LSTM-DNN model with grouped data reduces the RMSE value by 10% compared to the non-grouped LSTM-DNN model. Additionally, the LSTM-CNN-DNN model with grouped data reduces the RMSE value by 12% compared to the non-grouped LSTM-DNN model. These results demonstrate that the proposed method can reduce forecasting errors and assist in power dispatching.