SOLAR POWER FORECASTING USING DIFFERENT MACHINE LEARNING TECHNIQUES

Abstract

The Sun is an ultimate source of energy for all the objects living on Earth and solar radiation estimation is utmost important as this radiation serves as a primary energy source of conversion for the photovoltaic panels and the solar thermal power plants. This solar radiation is not constant in every region but it depends on various climatological parameters like temperature, wind-speed and many more, so there is intermittency in its behavior which results in changes in the electrical energy production. The above few statements reveal the necessity of predicting solar radiation. Physical method, Statistical method, Hybrid method are namely the methods put forward by the researchers around the globe for the purpose of forecasting Global Solar Radiation. The time-series forecasting method (ARIMA, ARX, AR models) are the oldest of all the methods thus there are countless research materials on them but in the recent years forecasting with the help of Machine Learning techniques (k-NN, SVM, Random Forest) have gained popularity over the time series method of forecasting. Today, the world is moving in a fast pace with regards to energy and power. It is something which is taken for granted. In order to suffice this demand of energy globally we are mostly dependent on non-renewable sources of energy which with its high-time usage is soon going to deplete and is a major cause of global warming. So, the world is looking for an environmentally friendly energy resources, the solar energy proves to be an important clean energy source. Entire requirements of human population can easily be met by the amount of solar energy that falls on the earth’s surface every hour. Thus, it becomes necessary for the industries to switch from traditional sources to solar energy as its primary source of energy and this requires an accurate prediction of solar power. The solar power forecasting not only determines the size of the operating reserves for generation-load balance but also reduces the operating cost thereby improving the reliability of the grid. In this research work, the 3 Machine Learning algorithms i.e., K-Nearest Neighbors, Support Vector Machine, Random Forest are used to build models for accurately predicting solar power of Kurnool region of Karnataka (India).At the tail end of the paper the evaluation metrics RMSE, MAPE, MAE etc. are also calculated to measure the accuracy of each algorithm and a significant comparison is made among them to come up on a conclusion that Random Forest method gave the best overall performance with RMSE=0.6157, MAPE=2.6421, MAE=0.4903 and R2=0.6517