What question did this study set out to answer?

The aim is to evaluate the predictive reliability of various machine learning models for photovoltaic system performance.

March 21, 2026Open Access

Forecasting Photovoltaic Performance: A Comparative Assessment of Machine Learning Methods

Key Points

The aim is to evaluate the predictive reliability of various machine learning models for photovoltaic system performance.
Created a simulation model using the System Advisor Model (SAM) with five years of weather data.
Analyzed datasets using machine learning algorithms like Linear Regression, Decision Tree, Random Forest, and XGBoost.
Assessed model accuracy using performance indicators such as MAE, MSE, RMSE, and R2.
Ensemble models like Random Forest and XGBoost achieved R2 values over 97% for output and temperature forecasts.
Bifacial systems outperformed monofacial systems in energy generation and thermal tolerance.
Global Horizontal Irradiance, temperature, and wind speed were identified as key performance determinants.

Abstract

The increasing global need for renewable energy sources has identified photovoltaic systems as essential to clean energy transition initiatives. This study examines the predictive reliability of machine learning models in assessing and forecasting the output and thermal performance of a medium-scale photovoltaic facility, employing both monofacial and bifacial modules in Canada. A simulation model was created utilizing the System Advisor Model (SAM) with five years of weather data to produce hourly outputs, including power and photovoltaic cell temperature. The datasets were analyzed utilizing multiple regression-based machine learning algorithms, such as Linear Regression, Polynomial Regression, Decision Tree, Random Forest, XGBoost, and regularization methods. Key performance indicators, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), were assessed to compare model accuracy. Results demonstrate substantial enhancements in prediction accuracy through the utilization of ensemble models such as Random Forest and XGBoost, attaining R2 values over 97% for both power output and cell temperature forecasts. Bifacial systems exhibited superior energy generation efficiency and tolerance to thermal fluctuations relative to monofacial systems, owing to their dual-sided light capture capability. Analysis of feature importance indicated that Global Horizontal Irradiance (GHI), temperature, and wind speed were the primary determinants affecting performance.

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