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The challenges in applications of solar energy lies in its intermittency anddependency on meteorological parameters such as; solar radiation, ambienttemperature, rainfall, wind-speed etc., and many other physical parameters likedust accumulation etc. Hence, it is important to estimate the amount of solarphotovoltaic (PV) power generation for a specific geographical location.Machine learning (ML) models have gained importance and are widely used forprediction of solar power plant performance. In this paper, the impact ofweather parameters on solar PV power generation is estimated by severalEnsemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for thefirst time. The performance of chosen ML algorithms is validated by fielddataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, acomplete test-bed framework has been designed for data mining as well as toselect appropriate learning models. It also supports feature selection andreduction for dataset to reduce space and time complexity of the learningmodels. The results demonstrate greater prediction accuracy of around 96% forStacking and Voting EML models. The proposed work is a generalized one and canbe very useful for predicting the performance of large-scale solar PV powerplants also.

Computational Solar Energy -- Ensemble Learning Methods for Prediction of Solar Power Generation based on Meteorological Parameters in Eastern India