Predicting Hourly Solar Irradiance Using Machine Learning Methods

Accurate prediction of solar irradiance for steady supply of electrical energy has always been a difficult task both in the field of physical simulation or artificial intelligence. If solar irradiance could be predicted with very high precision, it would be easier for power grid operators to take corrective actions in advance to ensure failsafe outcomes during periods of sudden or cascading drops in solar energy. In this paper, three machine learning techniques were used to forecast hourly solar irradiance of Johannesburg city using historical meteorological data of two consecutive years. The goal was to build a model that would readily utilize historical meteorological data to predict highly accurate solar irradiance information on an hourly basis. Results obtained showed that Support Vector Regression (SVR) with normalized Root Mean Square Error (nRMSE) of 7.2% gave the best overall performance. This was followed by Artificial Neural Network (ANN) and Random forest respectively.