Comparative Study of Regression Models for PV Power Prediction

Electric power production is a necessity and cannot be avoided. Thermal power plants are the primary sources of power generation, and they result in substantial emissions of carbon dioxide. Moreover, the depletion of fuels is also an issue of concern. As a result, solar energy is being promoted globally. The solar system’s behavior is stochastic and dynamic. A comparative study of the Poisson regression, Linear regression, and Lasso regression models has been done to predict the nature of such behavior and forecast the generated electric power by the solar plant, based on the weather parameters such as temperature, humidity, and luminous intensity. The Linear regression model predicts the output power of PV systems more accurately over the Poisson regression and Lasso regression models.