Enhanced Solar Power Prediction Using Attention-Based DiPLS-BiLSTM Model

Abstract

The data for solar power generation contain a huge amount of data with a large number of features which are difficult to extract effectively. It is important for the grid management and operational efficiency of the solar farm to accurately predict the solar power. The existing prediction models utilize historical data but often fail to capture critical latent features. This limitation leads to overlooked complex dependencies or temporal relationships, reducing prediction accuracy, especially in load and generation forecasting. An attention-based dynamic inner partial least squares (DiPLS) model and a bidirectional long short-term memory (BiLSTM) model were used for solar power prediction. First, DiPLS is used to dynamically extract features, and then, an attention process is used to predict the importance of these features. Finally, the raw sets are input to the BiLSTM model to make predictions of solar power in the future. The proposed method improves prediction accuracy, achieving an R2 value of 0.965 for training and 0.961 for testing, compared to conventional models. Additionally, the method demonstrated lower root mean squared error (RMSE), indicating enhanced stability and accuracy for solar power forecasting.