Two-Stage Hybrid Day-Ahead Solar Forecasting
This work addresses the need for accurate solar forecasting to mitigate grid vulnerabilities from renewable energy variability, but it appears incremental as it builds on existing forecasting methods with specific enhancements.
The paper tackles the problem of day-ahead solar forecasting by proposing a two-stage hybrid method that separates linear and nonlinear components and integrates data processing to handle nonstationarity, resulting in improved accuracy as demonstrated in numerical simulations on three test days with different weather conditions.
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.