Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman

Mazhar Baloch (), Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary and Muhammad Salman Saeed
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Mazhar Baloch: Department of Electrical Engineering & Computer Science, College of Engineering, A’Sharqiyah University, Ibra 400, Oman
Mohamed Shaik Honnurvali: Faculty of Engineering & Technology, Muscat University, Muscat 113, Oman
Adnan Kabbani: Department of Electrical Engineering & Computer Science, College of Engineering, A’Sharqiyah University, Ibra 400, Oman
Touqeer Ahmed: Department of Electrical Engineering & Computer Science, College of Engineering, A’Sharqiyah University, Ibra 400, Oman
Sohaib Tahir Chauhdary: Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah 201, Oman
Muhammad Salman Saeed: Multan Electric Power Company, Punjab 60000, Pakistan

Energies, 2025, vol. 18, issue 1, 1-22

Abstract: The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R 2 , and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R 2 values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman.

Keywords: solar energy forecasting; Prophet Algorithm; machine learning framework; Prophet ML model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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