Solar Irradiation Prediction Based on M5 Model Tree and Feature Importance Evaluation

Abstract

In the last decade, the usage of renewable energy is on the rise, and that trend will only continue because technology is becoming more developed, so renewable energy sources are going to offer more for the same price. Besides all positive properties, there are also some negatives like direct dependence on the weather conditions. That means energy production is constantly changing, so it must be as precisely as possible predicted to be usable on a large scale. Fifteen attributes were analyzed using M5 regression tree. High positive degree of correlation was found between participle water and dew point temperature, air temperature with dew point, air temperature with precipitation of water, snow depth with Albedo daily, zenith angle with relative humidity, GHI with Air temperature. It was found that the zenith angle, between the normal of the Earth’s surface and the Sun, was the most important feature of the dataset for solar irradiation prediction.