Prediction in The Solar Power Generation Based on Weather Forecasts Using Machine Learning

Abstract

A vital aim of intelligent grid schemes is to greatly increase the share of grid electricity that has renewable sources contribution. The fact that renewable solar is unreliable and unregulated is one of the problems of integrating in the grid.It is therefore necessary to forecast future renewable energy, because to meet demand, grid must disperse generators as the production variations.A major source of electricity generation has been PV system in renewable system.The uncertain climate activity impacts the power supply and has an adverse effect on grid stability, efficiency and the grid performance.   This paper proposes to forecast the output of solar power generated by solar power generators via an SVMbased on machine learning. Two major challenges result from the erratic nature of solar energy. In order to guarantee control over the whole system, first of all, electricity generation and demand must be balanced, and the inherent variability of clean energies finds it hard. Secondly, businesses in electricity generation require a highly reliable daily or intra-day energy forecast to be sold in the power pool.