Solar Illumination And Wind Speed Prediction: The Relative Prospects and Potential outcomes

Abstract

The Photovoltaic and wind are well-thought-out to be a clean source of power generation from Renewable Energy Sources (RES) without carbon emission which can relieve grid dependency. As we advance in both time and innovation, our vitality needs are ascending at an exponential level and henceforth we need to tap undependable sources of energy all the more proficiently. As the RES power are directly dependent on environmental conditions, the challenging task is to predict the power generation from them. Due to substantial increment in installation of PV and wind generation; prediction of power generation from these plays a vital role in having a better load control. The paper focuses on the prediction techniques to forecast solar illumination level and wind speed with the historical data of environmental conditions thereby power generated can be projected a day ahead. This task is achieved by statistical methods where large historical data of the environmental conditions of a particular location is collected. The data analyzed using Neural Network model, Regression Trees model, and multiple linear regression models. The results obtained are compared with the actual value available for validation.