A novel U-LSTM-AFT model for hourly solar irradiance forecasting

Abstract

Countries emphasize the development of renewable energy to combat climate change, ensure energy security, and promote sustainable economic growth. Grid-connected photovoltaic (PV) power generation, a key to energy transition, can significantly reduce power generation costs. Still, its output power is unstable due to the weather, so an accurate solar irradiance forecast is crucial. The current hybrid model mainly suffers from inadequate feature extraction and insufficient time-dependent processing when dealing with complex time series data, which affects prediction accuracy. Therefore, a new solar irradiance forecast model, U-Shaped LSTM-AFT (U-LSTM-AFT) is proposed in this paper, which draws on the architecture and ideas of U-Net. The model improves the efficiency of feature extraction through the up-sampling and down-sampling modules while combining with smaller pooling kernels to optimize feature processing further. In addition, the model introduces the Long Short-Term Memory Network (LSTM) and Attention-Free Transformer (AFT) to enhance prediction accuracy and achieve more efficient forecast performance. The method was validated using three real-world irradiance datasets from diverse US locations representing different climate types. The experimental results demonstrate that the proposed U-LSTM-AFT model achieves higher forecasting accuracy in predicting PV power generation than traditional models like LSTM and U-Net, with significant performance improvements. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), R-squared (R2), and Forecast Skill (FSnRMSE) are the performance evaluation metrics of this forecasting scheme. The three datasets reached mean values of 23.076 W/m2, 50.142 W/m2, 0.046, 98.680 %, and 0.238, respectively.