Nowcasting of Weather Parameters Impacting Solar PV Output Using Grey System Model

Abstract

Objectives: To nowcast the weather parameters having a direct impact on the power output of the solar PV installations, with high prediction accuracy and a limited quantity of past data. Methods: In this study, the GM (1,1) model with Fourier series of error residuals has been proposed and used for forecasting the weather parameters namely Ambient temperature, Solar Photo Voltaic Module temperature, and Solar Irradiation. Real-time data has been used for showing the suitability of the proposed model to nowcast the weather parameters. The existing models like Autoregression and Double Exponential Smoothing are applied to the same data to prove the superiority of the GM (1,1) model with Fourier series of error residuals. Findings: It is found that the GM (1,1) model with Fourier series of error residuals is an apt model for nowcasting the weather parameters. The accuracy of the predicted result of this model on the real-time data ascertains the appropriateness of this model for nowcasting. The precision of the prediction accuracy of GM (1,1) with the Fourier series of error residuals model is verified by comparing it with other time series prediction models such as Autoregression and Double Exponential Smoothing algorithms. Novelty: Using GM (1,1) with the Fourier series of error residuals model for nowcasting the weather data is novel when compared with the existing algorithms because for nowcasting the weather parameters with accuracy, many of the existing algorithms require a huge volume of past data and involve complex computation. On the other hand, GM (1,1) with the Fourier series of error residuals model requires only a limited measure of past data and involves simple computation. Moreover, the accuracy of prediction is significantly higher than the other models. Keywords: Grey Theory; Renewable Energy; PV Installation; Auto Regression; Double Exponential Smoothing