Solar Power Prediction Using Soft Voting Based Ensemble Machine Learning Classifier

The accurate forecasting of solar power is an essential component of both the effective incorporation of solar power into the existing electrical grid and the improvement of overall energy management. Using a Soft Voting based Ensemble Machine Learning Classifier is the innovative strategy that we present here for estimating the amount of electricity that will be generated by the sun. The Adaboost, KNN, Logistic Regression, and SVC machine learning classifiers are just few of the examples that are included in the ensemble model, which integrates the best aspects of numerous machine learning classifiers into one reliable and precise prediction system. The ensemble model is able to successfully represent the variability of solar energy production in response to changing weather conditions because it makes use of the many learning methodologies that these classifiers have to offer. The Soft Voting process makes certain that the predictions from the many individual classifiers are given the proper amount of weight, which in turn improves the overall predictive performance of the ensemble. Comprehensive tests are run on datasets collected from actual solar power production in the real world in order to evaluate the efficacy of the proposed technique. The findings show that the ensemble model is superior than individual classifiers. The Soft Voting based Ensemble Machine Learning Classifier is a promising and practical approach for increasing the accuracy of solar power prediction, which contributes to the smooth integration of renewable energy sources and to the management of sustainable energy. The accuracy of the model that was proposed was 91%.