A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and cloud property estimation

Solar energy is an increasing part of the energy portfolio in the United States. Accurate forecast of solar resource and power is essential for the management of the electricity grid, market operations, and reducing the cost of solar energy. High-frequent forecast of solar radiation in intra-hour horizons is important for real-time electric power system energy management especially in distribution level. The conventional numerical weather prediction (NWP) models perform poorly in intra-hour high-frequency forecasts because of the limits on real-time computing and the infrequent availability of observations. Although a number of alternative technologies, e.g. time series analysis and machine learning, have been utilized to fill this gap, the smart persistence model is among the top performing models in short-term forecasting and therefore often serves as the baseline to evaluate other forecasting models. However, obvious uncertainties exist in the current smart persistence model: (1) clear-sky index does not respond to the variation of solar incident angle when cloud condition is persistent within the forecasting horizon; and (2) cloud coverage is inherently persistent though it is constrained by cloud advection. In this study, we developed a physics-based smart persistence model for intra-hour solar forecasting (PSPI) that integrates cloud property estimation, radiative transfer models, and cloud fraction forecast to improve the performance of the smart persistence model. Compared to the smart persistence model, PSPI does not require additional observation of various atmospheric parameters but is customizable in that additional observations if available can be ingested to further improve the forecast. Our preliminary results show that PSPI outperforms the persistence and smart persistence models on a 30-minute forecast horizon. The software package of PSPI is flexible to users' needs and provides a low computational and time cost to run at site-specific locations across the continental United States. This result can be integrated into power system energy management platform and provide critical baseline standards for solar PV operation.