Standard of reference in operational day-ahead deterministic solar forecasting

Skill scores can be used to compare deterministic (also known as single-valued or point) forecasts made using different models at different locations and time periods. To compute the skill score, a reference forecasting method is needed. Nonetheless, there is no consensus on the choice of reference method. In this paper, three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario. Day-ahead global solar irradiance forecasts with an hourly resolution are generated using research-grade data from 32 sites around the globe, over a period of 1 year, in an operational manner. To avoid exaggerating the skill scores, it is generally agreed that the most accurate naive forecasting method should be chosen as the standard of reference. In this regard, the optimal convex combination of climatology and persistence is highly recommended to be used as the standard of reference for day-ahead solar forecasting. Skill scores can be used to compare deterministic (also known as single-valued or point) forecasts made using different models at different locations and time periods. To compute the skill score, a reference forecasting method is needed. Nonetheless, there is no consensus on the choice of reference method. In this paper, three classes of commonly used references methods, namely, climatology, persistence, and their linear combination, are studied in a day-ahead solar forecasting scenario. Day-ahead global solar irradiance forecasts with an hourly resolution are generated using research-grade data from 32 sites around the globe, over a period of 1 year, in an operational manner. To avoid exaggerating the skill scores, it is generally agreed that the most accurate naive forecasting method should be chosen as the standard of reference. In this regard, the optimal convex combination of climatology and persisten...

[1]  Philippe Lauret,et al.  Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models , 2016 .

[2]  Henrik Madsen,et al.  Multi-site solar power forecasting using gradient boosted regression trees , 2017 .

[3]  Jan Kleissl,et al.  Operational solar forecasting for the real-time market , 2019, International Journal of Forecasting.

[4]  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.

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

[6]  Richard Perez,et al.  Can we gauge forecasts using satellite-derived solar irradiance? , 2019, Journal of Renewable and Sustainable Energy.

[7]  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.

[8]  Hosni Ghedira,et al.  Prediction of the day-ahead clear-sky downwelling surface solar irradiances using the REST2 model and WRF-CHIMERE simulations over the Arabian Peninsula , 2018 .

[9]  Nicholas A. Engerer,et al.  Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis , 2019, Renewable and Sustainable Energy Reviews.

[10]  Dipti Srinivasan,et al.  Reconciling solar forecasts: Sequential reconciliation , 2019, Solar Energy.

[11]  A. H. Murphy,et al.  Diagnostic Verification of Temperature Forecasts , 1989 .

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

[13]  Dazhi Yang,et al.  Post-processing of NWP forecasts using ground or satellite-derived data through kernel conditional density estimation , 2019, Journal of Renewable and Sustainable Energy.

[14]  P. Blanc,et al.  Towards a standardized procedure to assess solar forecast accuracy: A new ramp and time alignment metric , 2017 .

[15]  R. Hilborn.  Sea gulls, butterflies, and grasshoppers: A brief history of the butterfly effect in nonlinear dynamics , 2004 .

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

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

[18]  Taiping Zhang,et al.  Assessment of BSRN radiation records for the computation of monthly means , 2010 .

[19]  Dazhi Yang,et al.  Operational photovoltaics power forecasting using seasonal time series ensemble , 2018 .

[20]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[21]  Gokhan Mert Yagli,et al.  Quality Control for Solar Irradiance Data , 2018, 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia).

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

[23]  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.

[24]  C. Coimbra,et al.  Proposed Metric for Evaluation of Solar Forecasting Models , 2013 .

[25]  A. H. Murphy,et al.  Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient , 1988 .

[26]  Bri-Mathias Hodge,et al.  A suite of metrics for assessing the performance of solar power forecasting , 2015 .

[27]  Yugang Niu,et al.  Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM , 2018 .

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

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