A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

[1]  G. Notton,et al.  Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting , 2018 .

[2]  Serge J. Belongie,et al.  Cloud motion and stability estimation for intra-hour solar forecasting , 2015 .

[3]  Robin Girard,et al.  Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting , 2019, IEEE Transactions on Sustainable Energy.

[4]  L. Wald,et al.  Performance of CAMS Radiation Service and HelioClim-3 databases of solar radiation at surface: evaluating the spatial variation in Germany , 2020 .

[5]  A. Raftery,et al.  Probabilistic forecasts, calibration and sharpness , 2007 .

[6]  Dan Keun Sung,et al.  Solar Power Prediction Based on Satellite Images and Support Vector Machine , 2016, IEEE Transactions on Sustainable Energy.

[7]  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.

[8]  Matteo De Felice,et al.  Data-driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data , 2017 .

[9]  Mathieu David,et al.  Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data , 2018, International Journal of Forecasting.

[10]  Robin Girard,et al.  A stochastic optimal power flow for scheduling flexible resources in microgrids operation , 2018 .

[11]  A. Hammer,et al.  PV Power Predictions on Different Spatial and Temporal Scales Integrating PV Measurements, Satellite Data and Numerical Weather Predictions , 2014 .

[12]  F. J. Martinez-de-Pison,et al.  The value of day-ahead forecasting for photovoltaics in the Spanish electricity market , 2017 .

[13]  John Boland,et al.  Spatial-temporal forecasting of solar radiation , 2015 .

[14]  Dazhi Yang,et al.  Probabilistic solar forecasting benchmarks on a standardized dataset at Folsom, California , 2020 .

[15]  Bri-Mathias Hodge,et al.  Benchmark probabilistic solar forecasts: Characteristics and recommendations , 2020 .

[16]  Olivier Pannekoucke,et al.  A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production , 2014 .

[17]  L. Wald,et al.  The method Heliosat-2 for deriving shortwave solar radiation from satellite images , 2004 .

[18]  Joakim Munkhammar,et al.  Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model , 2019, Solar Energy.

[19]  S. Cros,et al.  Reliability Predictors for Solar Irradiance Satellite-Based Forecast , 2020, Energies.

[20]  B. Hodge,et al.  The value of day-ahead solar power forecasting improvement , 2016 .

[21]  M. David,et al.  Solar irradiation forecasting: state-of-the-art and proposition for future developments for small-scale insular grids , 2012 .

[22]  Tomonobu Senjyu,et al.  Multi objective unit commitment with voltage stability and PV uncertainty , 2018, Applied Energy.

[23]  Vladimiro Miranda,et al.  Probabilistic solar power forecasting in smart grids using distributed information , 2015 .

[24]  Jing Wu,et al.  Integrating solar PV (photovoltaics) in utility system operations: Analytical framework and Arizona case study , 2015 .

[25]  Emanuele Ogliari,et al.  Advanced Methods for Photovoltaic Output Power Forecasting: A Review , 2020, Applied Sciences.

[26]  E. Worrell,et al.  Assessment of forecasting methods on performance of photovoltaic-battery systems , 2018, Applied Energy.

[27]  E. Lorenz,et al.  Chapter 11 – Satellite-Based Irradiance and Power Forecasting for the German Energy Market , 2013 .

[28]  Minho Kim,et al.  Short-Term Forecasting of Photovoltaic Power Integrating Multi-Temporal Meteorological Satellite Imagery in Deep Neural Network , 2019, 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[29]  Jie Zhang,et al.  A review on the integration of probabilistic solar forecasting in power systems , 2020, Solar Energy.

[30]  Thomas Carriere,et al.  A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames , 2020, IEEE Transactions on Smart Grid.

[31]  Bri-Mathias Hodge,et al.  Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry , 2017 .

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

[33]  Hadrien Verbois,et al.  Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics , 2020 .

[34]  Leonard A. Smith,et al.  Increasing the Reliability of Reliability Diagrams , 2007 .

[35]  Andrea Michiorri,et al.  Optimal Offer of Automatic Frequency Restoration Reserve From a Combined PV/Wind Virtual Power Plant , 2018, IEEE Transactions on Power Systems.

[36]  L. Wald,et al.  Comparison of several satellite-derived databases of surface solar radiation against ground measurement in Morocco , 2018 .

[37]  H. Beyer,et al.  Solar energy assessment using remote sensing technologies , 2003 .

[38]  E. Caamaño-Martín,et al.  Improving photovoltaics grid integration through short time forecasting and self-consumption , 2014 .

[39]  L. Wald,et al.  Validation of the new HelioClim-3 version 4 real-time and short-term forecast service using 14 BSRN stations , 2016 .

[40]  John Boland,et al.  Nonparametric short-term probabilistic forecasting for solar radiation , 2016 .

[41]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

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

[43]  Joakim Widén,et al.  Review on probabilistic forecasting of photovoltaic power production and electricity consumption , 2018 .

[44]  L. Wald,et al.  Validation of HelioClim-3 Version 4, HelioClim-3 Version 5 and MACC-RAD Using 14 BSRN Stations , 2016 .

[45]  Mihai Anitescu,et al.  Data-driven model for solar irradiation based on satellite observations , 2014 .

[46]  Mario Paolone,et al.  Model-free computation of ultra-short-term prediction intervals of solar irradiance , 2016 .

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

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

[49]  M. David,et al.  Intra-day solar probabilistic forecasts including local short-term variability and satellite information , 2020 .

[50]  Pierre Pinson,et al.  Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy , 2016, IEEE Transactions on Power Systems.

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

[52]  Yih-huei Wan,et al.  Dark Shadows , 2011, IEEE Power and Energy Magazine.

[53]  Pierre Pinson,et al.  Verification of solar irradiance probabilistic forecasts , 2019 .