Estimation of energy production of solar panels installed in agricultural areas with machine learning algorithms

Predicting solar power generation is used to ensure that solar power plants operate with optimum efficiency, meet the demands of the energy grid and stabilize energy prices. This study aims to predict the medium-term electricity generation of photovoltaic panels with machine learning algorithms. Boosting Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Neural Network Regression, Random Forest Regression, Regularized Linear Regression, and Support Vector Machine Regression algorithms were evaluated. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared (R²) were calculated. It was found that the Random Forest algorithm has the best prediction metrics. A hypothesis was formulated to evaluate the difference between the actual energy generation of the photovoltaic panels and the predicted energy by the Random Forest algorithm. The hypothesis was evaluated by the Mann-Whitney U hypothesis test and the p-value was calculated as greater than 0.05. It was concluded that there is no significant difference between the predicted energy by the Random Forest (RF) algorithm and the actual energy generated by photovoltaic panels. Based on the results of this study, we recommend using the Random Forest algorithm for medium-term energy generation prediction for photovoltaic solar panels.