Illuminating the Future: Predictive Modelling of PV Output Using Machine Learning Techniques

Abstract

Leveraging solar energy will bring about a notable change in the fundamental production and use of power, and the parameters to achieve success in this area must be forecasted to aid steady production. This work entailed the use of advanced predictive machine learning models for optimal power output, reduced uncertainty, optimal resource planning, and a notably high degree of alignment with peak demands for energy for efficient power production from solar radiations. Models were generated by employing machine learning algorithms for data evaluation. The direct in-plane irradiance has the strongest correlation (1.00) with PV output, according to the results. Additionally, it indicated that the value of R2: 0.999567 of the Random Forest Regression was higher than all other regression models and the least Mean Squared Error (MSE) and Mean Absolute Error (MAE), 17.130680 and 2.28139, respectively. On the other hand, the Linear Regression’s Mean Squared Error (MSE), R2, and Mean Absolute Error (MAE) values are, respectively, 20.645271, 0.999478, and 3.16270. Random Forest Regression is a stronger forecasting model because of its higher R2 value, which also helps to explain variations in PV power output.