A pyramidal attention-based transformer model based on improved differential innovation search algorithm and feature extraction for solar radiation prediction considering relevant factors

Accurate prediction of solar radiation intensity (SI) is crucial for power system scheduling and site selection of photovoltaic power stations. This paper proposes a multivariable solar radiation prediction model based on Time-varying Filter-based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Random Forest (RF), Triangular Wandering Strategy Improved Differential Creative Search Algorithm (TDCS), and Pyramidal Attention-based Transformer (Pyraformer). First, the Random Forest is used for feature extraction of solar radiation data; then, TVFEMD is employed to break down solar radiation data into constituent sub-modes, thereby mitigating the non-stationarity present in the data sequence. Fuzzy Entropy-based aggregation is utilized to diminish the quantity of data sequences, which, together with various features, forms a multivariable input feature matrix. The Differential Creative Search Algorithm (DCS) is enhanced with a Triangular Wandering Strategy to obtain Improved TDCS, which optimizes the hyperparameters of Pyraformer for solar radiation prediction, thereby enhancing the model's predictive performance. This paper analyzes the prediction metrics of the TDCS-RF-TVFEMD-FE-Pyraformer multivariate model compared with nine other multivariate benchmark models. The results show TVFEMD, FE, and RF boost models accuracy. Post TDCS optimization, Pyraformer's RMSE and MAE outperform the baseline models by 10 %–50 %, with R and SMAPE also outperforming the baseline models.