Estimation of Solar PV Power Plant Output Using Machine Learning Algorithms

Photovoltaic (PV) systems have become ubiquitous worldwide, harnessing solar energy to generate power on a global scale. Solar output power is varying and largely dependent on environmental factors. These factors consist of humidity, wind speed, irradiance, and the PV module surface temperature, etc. Since photovoltaic generation is not constant in nature, it is critical to prepare for contingencies related to its generation, as the prediction of solar power plant output is important for the electric grid to be designed to accommodate any fluctuations. This paper delves into an examination of how diverse environmental factors impact the output of a solar PV system. Additionally, it explores the application of various Machine Learning (ML) algorithms, including Linear regression, Polynomial regression, Ridge regression, and Lasso regression techniques. Recurrent Neural Network are also implemented on the dataset discussed in [10] to forecast the solar power output. The effectiveness of these techniques is evaluated using nRMSE values. Out of these techniques, polynomial regression outperformed the other regression models with high accuracy.