Patch Time Series Transformer−Based Short−Term Photovoltaic Power Prediction Enhanced by Artificial Fish

The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short−term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish Swarm Algorithm–Isolation Forest (AFSA–IF) anomaly detection, Generative Adversarial Network−based feature extraction, multimodal data fusion, and a Patch Time Series Transformer (PatchTST) model. The framework includes advanced preprocessing, fusion of meteorological and historical power data, and weather classification via one−hot encoding. Experiments on datasets from…