Description

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. Solar radiation estimation determines how much energy the sun provides to a particular region. This radiation is the primary energy source of conversion in photovoltaic plants and solar thermal power plants. The incident radiation is not constant and depends on climatic data, which results in an intermittency in its behavior and changes in the production of electrical energy are observed. This justifies the development of a tool for predicting and estimating incident radiation in order to foresee changes in the performance of photovoltaic generation systems. In this process we proposes the machine learning algorithms like linear regression and support vector regression algorithms are implemented and forecast the dataset by predicting global horizontal irradiation from solar. The Machine Learning technique is analysis the solar irradiance dataset and generated the forecast results like mean squared error, root mean squared error and mean absolute error.