Estimation of Solar Power Generation using Ensemble Learning and Algebraic Combiners

As a result of today's pollution, renewable and ecofriendly energy sources have received increased attention in recent years. Before constructing a new solar power plant, it is necessary to study the breakeven point and power generation. So far., solar power estimation models have been developed to guarantee the viability of a new power plant. This paper presented a technique for estimating solar power based on ensemble learning and algebraic combiners. Three different models, i.e. Nearest Centroid Classifier (NCC), Support Vector Machine (SVM), and Decision Tree Classifier (DTC), are also studied and evaluated in this work. Compared to the other techniques, the suggested method with the Weighted Sum Rule (WSR) delivers the most accurate benchmark result, with an accuracy of 99.915%.