Two-Stage Hybrid Day-Ahead Solar Forecasting

TL;DR

This paper introduces a two-stage hybrid approach for day-ahead solar forecasting that combines linear and nonlinear models, enhanced by data processing techniques, to improve accuracy under varying weather conditions.

Contribution

It presents a novel two-stage hybrid forecasting method with integrated data processing to address nonstationarity and improve solar prediction accuracy.

Findings

Effective in different weather conditions

Reduces forecasting errors

Outperforms traditional methods

Abstract

Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.