Novel model for medium to long term photovoltaic power prediction using interactive feature trend transformer

The stochastic and variable nature of power generated by photovoltaic (PV) systems can impact grid stability. Accurately predicting the output power of a solar PV power generation system is crucial for addressing this challenge. While short-term PV power prediction is highly accurate, the accuracy of medium- to long-term photovoltaic power predictions will face great challenges. In order to improve the accuracy of medium and long-term photovoltaic power prediction, a unique hybrid deep learning model named interactive feature trend transformer (IFTformer) has been designed. Initially, deep isolated forest (DIF) and local anomaly factor (LOF) are used to construct a parallel framework that serves as the data preprocessing module, removing outliers from raw data. The time series are subsequently decomposed into seasonal and trend components, which are modelled separately for independent study. Ultimately, the predicted trend components with the seasonal components predicted by the ProSparse Self-attention mechanism based on information interaction are fitted by the multilayer perceptron (MLP) for medium- to long-term PV power prediction. The comprehensive experimental results show that the predictive performance of IFTformer is superior to that of baseline models, with a normalised root mean square error (NRMSE) of 3.64% and a normalised mean absolute error (NMAE) of 2.44%. The IFTformer model proposed in this paper is an effective approach for medium- to long-term PV power prediction, can mitigate the impact of outliers, enhance the feature extraction ability, and improve the prediction accuracy, generalizability and robustness of medium- to long-term predictions, providing a novel perspective on medium- to long-term PV power prediction methods based on deep learning methods.