Exploring and forecasting of solar radiation with machine learning methods

Abstract

Machine learning has recently advanced to the point that a wide range of solar predicting works have been produced. Specifically, one of the most widely used methods at the moment for hourly solar forecasting is machine learning. However, it appears that there is a misconception regarding forecast accuracy—almost all study articles assert to be better than others. However, it appears that there is a misconception regarding forecast accuracy—almost all study articles assert to be better than others. It is illogical for solar forecasters to place their initial wager on a single model for any new forecasting project. Only commercially available versions of these algorithms are employed, and no hybrid models are taken into account to guarantee an equitable comparison. Additionally, the package's automatic tuning algorithm is used to train each model. Overall results show that tree-based methods consistently yield good results. Reliable generation forecasts are becoming more and more necessary for grid operation as distributed renewable power grows in penetration. In order to produce the most accurate day-ahead hourly irradiance forecasts, the current work combines cutting edge Weather Research and Forecasting (WRF) model implementations with machine learning techniques.