Paper Title
Solar Power Forecasting using different Machine Learning Algorithms (kNN, SVM, Random Forest)

Abstract - 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 planetary surface every hour. Thus, it becomes necessary for the industries to leap from traditional sources to solar energy as its key 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 paper, 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. Keywords - Solar Power Forecasting, Operating Reserves, Random Forest, Support Vector Machine, RMSE, MAPE