Smart Modeling of Photovoltaic Production Based on Meteorological Data and Production Capacity: Utilizing Advanced Machine Learning Algorithms

This study investigates the prediction of photovoltaic (PV) energy production using advanced machine learning algorithms, leveraging meteorological data and production capacity from ۳۰۰ residential PV plants in Sydney, Australia. The dataset was processed into daily values to account for weather variability, and three machine learning models (Random Forest Regression (RFR), Support Vector Regression (SVR), and Light Gradient Boosting Regression (LightGBR)) were implemented. Following rigorous preprocessing and hyperparameter optimization, LightGBR exhibited superior predictive performance, achieving a coefficient of determination (R²) of ۰.۹۰۲۰, a mean absolute error (MAE) of ۳.۱۶۲۱, and a mean squared error (MSE) of ۰.۱۰۰۵. Compared to previous studies, the optimized LightGBR model demonstrated enhanced accuracy in PV energy forecasting, underscoring its potential for improving predictive modeling in this domain. These findings have significant implications for optimizing energy distribution, enhancing smart grid integration, and supporting decision-making in energy management systems. Accurate forecasting of PV energy output is essential for improving operational efficiency, minimizing energy waste, and advancing sustainability objectives in renewable energy management.

کلیدواژه ها:

Forecasting ، Photovoltaic Systems ، Machine learning ، Random Forest Algorithm ، Support Vector Regression (SVR) ، Light Gradient Boosting Regression (LightGBR)