Predictive Modeling and Forecasting of Solar Power Generation Using Machine Learning Techniques

Abstract

The purpose of this research study is to investigate the predictive modelling and forecasting of solar power generation by utilising a variety of machine learning approaches. The paper addresses the significant obstacle that is presented by the variable production of solar energy, which is a barrier to the effective incorporation of solar power into the network of electrical power distribution systems. This research investigates the application of machine learning models such as Bayesian Ridge Regression, Gradient Boosting, and Linear Regression. The research makes use of historical meteorological data and solar power output to investigate the application of these models. The effectiveness of these models in estimating the amount of solar energy that will be produced under a variety of different weather situations is being investigated. Based on the findings, it is evident that the application of machine learning techniques has the potential to considerably improve the accuracy of solar power projections. This, in turn, will facilitate better grid integration and promote wider adoption of solar energy products. Through the demonstration of the potential of advanced machine learning approaches in enhancing the reliability and efficiency of solar power generation, this study makes a contribution to the expanding body of literature on the forecasting of renewable energy sources.