PV Output Power Prediction Using Regression Methods

Over the past few years, the general public has become increasingly aware of climate change and the role of greenhouse gas emissions, especially carbon dioxide, in contributing to it. Therefore, individuals, businesses, and governments around the world have taken steps to reduce their emissions. One of these steps is to increase adoption of renewable energy sources, such as solar power which provides clean energy, in addition to low building and operation costs and minimal maintenance requirements. Accurate estimation of solar energy production is crucial to ensure the stability of electrical networks as the transition to renewable energy sources such as solar power increases. In this study, machine learning regression algorithms including artificial neural networks, support vector regression, regression trees, and k-nearest neighbor are performed to estimate hourly solar energy production of one month using historical production data and various meteorological parameters. The models are optimized using grid search and validated using K-fold cross validation method. The performance of the models is evaluated using the RMSE, MAE, and R2 evaluation metrics. The results showed that the k-nearest neighbor regression model achieves the highest performance with an R2 score of 0.9715.