A Monte Carlo based solar radiation forecastability estimation

Based on the reported literature and commonly used metrics in the realm of solar forecasting, a new methodology is developed for estimating a metric called forecastability (F). It reveals the extent to which solar radiation time series can be forecasted and provides the crucial context for judging the inherent difficulty associated with a particular forecast situation. Unlike the score given by the standard smart persistence model, the F metric which is bounded between 0% and 100% is easier to interpret, hence making comparisons between forecasting studies more consistent. This approach uses the Monte Carlo method and estimates F from the standard error metric RMSE and the persistence predictor. Based on the time series of solar radiation measured at six very different locations (with optimized clear sky model) from a meteorological point of view, it is shown that F varies between 25.5% and 68.2% and that it exists a link between forecastability and errors obtained by machine learning prediction methods. The proposed methodology is validated for 3 parameters that may affect the F estimation (time horizon, temporal granularity, and solar radiation components) and for 50 time series relative to McClear web service and to the central archive of Baseline Surface Radiation Network.

[1]  David Ruelle.  Chaos, predictability, and idealization in physics , 1997, Complex..

[2]  Francisco J. Santos-Alamillos,et al.  Analysis of the intra-day solar resource variability in the Iberian Peninsula , 2018, Solar Energy.

[3]  Michel Fliess,et al.  Prediction bands for solar energy: New short-term time series forecasting techniques , 2018 .

[4]  T. Soubdhan,et al.  A benchmarking of machine learning techniques for solar radiation forecasting in an insular context , 2015 .

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

[6]  G. Abal,et al.  Performance of the site-adapted CAMS database and locally adjusted cloud index models for estimating global solar horizontal irradiation over the Pampa Húmeda , 2020 .

[7]  G. Lohmann,et al.  Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review , 2018, Atmosphere.

[8]  R. Rüther,et al.  Extreme solar overirradiance events: Occurrence and impacts on utility-scale photovoltaic power plants in Brazil , 2019, Solar Energy.

[9]  Dazhi Yang,et al.  Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years , 2020 .

[10]  Chang-Yeol Yun,et al.  Probabilistic prediction of direct normal irradiance derived from global horizontal irradiance over the Korean Peninsula by using Monte-Carlo simulation , 2019, Solar Energy.

[11]  G. Notton,et al.  Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components , 2019, Renewable Energy.

[12]  P. Ineichen.  A broadband simplified version of the Solis clear sky model , 2008 .

[13]  C. Gueymard.  Clear-Sky Radiation Models and Aerosol Effects , 2019, Solar Resources Mapping.

[14]  Michel Dekking,et al.  A Modern Introduction to Probability and Statistics: Understanding Why and How , 2007 .

[15]  Gil Zussman,et al.  Irradiance Field Reconstruction From Partial Observability of Solar Radiation , 2019, IEEE Geoscience and Remote Sensing Letters.

[16]  Can Wang,et al.  A Copula Function Based Monte Carlo Simulation Method of Multivariate Wind Speed and PV Power Spatio-Temporal Series , 2019 .

[17]  Cyril Voyant,et al.  Statistical parameters as a means to a priori assess the accuracy of solar forecasting models , 2015 .

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

[19]  S. Cros,et al.  Clear Sky Models Assessment for an Operational PV Production Forecasting Solution , 2013 .

[20]  Katalin Bódis,et al.  Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach , 2014 .

[21]  Jean-Laurent Duchaud,et al.  Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability , 2018, Energy.

[22]  D. Kumar,et al.  Hyper-temporal variability analysis of solar insolation with respect to local seasons , 2019, Remote Sensing Applications: Society and Environment.

[23]  Dazhi Yang,et al.  Choice of clear-sky model in solar forecasting , 2020 .

[24]  Dazhi Yang,et al.  Making reference solar forecasts with climatology, persistence, and their optimal convex combination , 2019, Solar Energy.

[25]  Frederik Michel Dekking,et al.  A Modern Introduction to Probability and Statistics , 2005 .

[26]  Richard Perez.  Chapter 6 – Solar Resource Variability , 2013 .

[27]  Gilles Stoltz,et al.  Fundamentals and Exchange Rate Forecastability with Simple Machine Learning Methods , 2018, Journal of International Money and Finance.

[28]  Dennis A. Ahlburg,et al.  Error measures and the choice of a forecast method , 1992 .

[29]  Richard Perez,et al.  Characterization of the intraday variability regime of solar irradiation of climatically distinct locations , 2016 .

[30]  Dirk Nuyens,et al.  Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications , 2019, Reliab. Eng. Syst. Saf..

[31]  Marius Paulescu,et al.  Quantifiers for the solar irradiance variability: A new perspective , 2018, Solar Energy.

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

[33]  H. Pedro,et al.  Short-term irradiance forecastability for various solar micro-climates , 2015 .

[34]  M. Topič,et al.  Methodology of Köppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance , 2019, Solar Energy.

[35]  T. Hoff,et al.  QUANTIFYING PV POWER OUTPUT VARIABILITY , 2010 .

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

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

[38]  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.

[39]  Timothy DelSole,et al.  Predictability and Information Theory. Part I: Measures of Predictability , 2004 .

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

[41]  Clive W. J. Granger,et al.  Forecasting transformed series , 1976 .

[42]  F. Anctil,et al.  Comparison of empirical daily surface incoming solar radiation models , 2008 .

[43]  Francis X. Diebold,et al.  Measuring Predictability: Theory and Macroeconomic Applications , 1997 .

[44]  Matthew J. Reno,et al.  Solar variability zones: Satellite-derived zones that represent high-frequency ground variability , 2017 .

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

[46]  Arun Kumar,et al.  Inherent Predictability, Requirements on the Ensemble Size, and Complementarity , 2015 .

[47]  Murilo S Baptista,et al.  Space-time nature of causality. , 2016, Chaos.

[48]  Jan Kleissl,et al.  Solar variability of four sites across the state of Colorado , 2010 .