Enhanced Short-Term Photovoltaic Power Prediction using a Hybrid Improved Whale Optimization Algorithm with XGBoost

Grid-connected photovoltaic (PV) systems are becoming more and more popular in the renewable energy space, but their volatility and unpredictability present difficulties, especially in the event of variable weather. In order to address these issues, accurate PV power forecasting becomes essential for efficient grid management. In order to address this, we present a model called IMWOA-XGB, which combines XGBoost and the Improved Whale Optimization Algorithm (IMWOA). With the help of our model, PV power projections should be more accurate, allowing for the creation of well-informed generation plans that reduce the grid effect of PV systems. The root mean square error (RMSE) is 18.867 and the mean absolute error (MAE) is 8.639 for the suggested model.