An intelligent hybrid approach for photovoltaic power forecasting using enhanced chaos game optimization algorithm and Locality sensitive hashing based Informer model

Highly accurate photovoltaic power forecasting is of great significance for increasing photovoltaic (PV) power generation on urban building rooftops. In this research context, a method based on variational mode decomposition (VMD), an enhanced chaos game optimization (ECGO) algorithm, and an improved Informer model is proposed for predicting PV power. First, the data are decomposed using VMD. Then the Informer model is improved using locality sensitive hashing (LSH) attention. In this study, an attempt is made to incorporate LSH to find the nearest few key vectors for query vectors, which enhances the accuracy of the model for prediction. The chaos game optimization (CGO) algorithm is then enhanced using the circle chaos initialization and golden sine algorithm. The Circle Chaos Initialization replaces the random initialization in the CGO algorithm, and the golden sine algorithm updates the position of the fourth seed in the CGO. The hyperparameters of the model proposed in this study were then optimized using an Enhanced Chaos Game Optimization (ECGO) algorithm. The experimental results show that the RMSE for April, May, and June data are improved by about 45.41%, 62.30%, and 50.52%, respectively, compared to Informer. In summary, the VMD-ECGO-LSH-Informer model can effectively improve the prediction accuracy of PV power generation, which provides important support and valuable contribution to improving the efficiency of rooftop PV power generation installations in urban buildings.