This method improves energy generation predictions in solar plants using meteorological data, indicating optimal forecasting techniques.
Key Points
Using k-Nearest Neighbors for forecasting solar energy generation achieved an R2 of 0.99 with local data, demonstrating the model's accuracy.
The study used meteorological datasets from on-site weather stations and satellite sources, finding local data significantly enhances prediction accuracy.
Statistical methods were employed to assess meteorological parameter significance, highlighting important environmental influences on forecasting performance.
The integration of precise meteorological measurements into prediction systems supports improved stability in power supply networks.