Enhancing PV power forecasting through feature selection and artificial neural networks: a case study

of 0.946 when using Chi-square selected features. These findings confirm that careful feature selection enhances model performance, reduces complexity, and ensures better generalization, offering valuable insights for more efficient solar energy management and grid stability.