On the importance of clearsky model in short-term solar radiation forecasting

Clearsky models are widely used in solar energy for many applications such as quality control , resource assessment, satellite-base irradiance estimation and forecasting. However, their use in forecasting and nowcasting is associated with a number of challenges. Synchronization errors, reliance on the Clearsky index (ratio of the global horizontal irradiance to its cloud-free counterpart) and high sensitivity of the clearsky model to errors in aerosol optical depth at low solar elevation limit their added value in real-time applications. This paper explores the feasibility of short-term forecasting without relying on a clearsky model. We propose a Clearsky-Free forecasting approach using Extreme Learning Machine ( ELM ) models. ELM learns daily periodicity and local variability directly from raw Global Horizontal Irradiance ( GHI ) data. It eliminates the need for Clearsky normalization, simplifying the forecasting process and improving scalability. Our approach is a non-linear adaptative statistical method that implicitly learns the irradiance in cloud-free conditions removing the need for an clear-sky model and the related operational issues. Deterministic and probabilistic results are compared to traditional benchmarks, including ARMA with McClear -generated Clearsky data and quantile regression for probabilistic forecasts. ELM matches or outperforms these methods, providing accurate predictions and robust uncertainty quantification . This approach offers a simple, efficient solution for real-time solar forecasting . By overcoming the stationarization process limitations based on usual multiplicative scheme Clearsky models, it provides a flexible and reliable framework for modern energy systems . • Novel method using raw GHI and machine learning, outperforming Clearsky-Based models. • Clearsky-Free and Based models concerning deterministic and probabilistic concerns. • Avoiding the Clearsky Index reduces dependency on complex intermediate calculations. • Enables smarter grids, energy trading, and renewable integration.