Making reference solar forecasts with climatology, persistence, and their optimal convex combination

Abstract Climatology and persistence are the two most popular naive reference methods for benchmarking deterministic solar forecasts. Depending on the forecast horizon, the preferred reference methods differ. This brief note derives the general relationship among climatology, persistence, and their optimal convex combination. The value of the lag-h autocorrelation is shown to be the main indicator of choice for the standard of reference.

[1]  G. Hodges,et al.  Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017) , 2018, Earth System Science Data.

[2]  Dazhi Yang,et al.  A universal benchmarking method for probabilistic solar irradiance forecasting , 2019, Solar Energy.

[3]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[4]  Jian Ma,et al.  Incorporating Uncertainty of Wind Power Generation Forecast Into Power System Operation, Dispatch, and Unit Commitment Procedures , 2011, IEEE Transactions on Sustainable Energy.

[5]  A. H. Murphy,et al.  A General Framework for Forecast Verification , 1987 .

[6]  Dazhi Yang,et al.  SolarData: An R package for easy access of publicly available solar datasets , 2018, Solar Energy.

[7]  Dipti Srinivasan,et al.  Automatic hourly solar forecasting using machine learning models , 2019, Renewable and Sustainable Energy Reviews.

[8]  T. Gneiting.  Making and Evaluating Point Forecasts , 2009, 0912.0902.

[9]  Dazhi Yang,et al.  SolarData package update v1.1: R functions for easy access of Baseline Surface Radiation Network (BSRN) , 2019, Solar Energy.

[10]  A. H. Murphy.  Climatology, Persistence, and Their Linear Combination as Standards of Reference in Skill Scores , 1992 .

[11]  Dazhi Yang,et al.  A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES) , 2019, Journal of Renewable and Sustainable Energy.

[12]  Johannes W. Kaiser,et al.  McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions , 2013 .

[13]  Gilles Notton,et al.  Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool , 2018, Renewable and Sustainable Energy Reviews.

[14]  Viorel Badescu,et al.  A current perspective on the accuracy of incoming solar energy forecasting , 2019, Progress in Energy and Combustion Science.

[15]  J. A. Ruiz-Arias,et al.  Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe , 2013 .

[16]  Carlos F.M. Coimbra,et al.  History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining , 2018, Solar Energy.