Application of Machine Learning to Forecast Solar Photovoltaic Output Power

Abstract

Due to the intermittent behaviour of the sun, accurate prediction of solar photovoltaic (PV) power is crucial for efficient and reliable operation of solar power plants. This paper presents state of the art approach for PV panels power prediction using machine learning (ML) method. Two ML models, namely Random Forest (RF) and Support Vector Machine (SVM) are trained and tested using input data of solar irradiance, ambient temperature, wind speed, humidity, precipitation and PV output power. The case study is presented for the grid tied PV system installed at University Tun Hussein Onn campus Batu Pahat Malaysia. The results indicated regression predictions reasonably fit the actual data, proving good potential of ML for PV power prediction. Besides, the predictive performance of RF and SVM was compared based on three evaluation metrics: coefficient of determination (R²), root mean squared error (RMSE) and mean absolute error (MAE). Both ML models showed comparable predictive power with RF performing slightly better than SVM. The R² value for RF was 0.850 compared to 0.832 for SVM, indicating that RF was able to explain more of the variability in the data. Additionally, RF had lower values for both RMSE and MAE, indicating that it was better able to predict values of the solar PV power output. The conclusion from this study imparts the importance of ML methods to predict PV power which could be useful for optimizing the efficiency and reliability of solar energy systems.