Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning

Abstract Increasing penetration of distributed renewable power means that reliable generation forecasts are required for grid operation. The present work aims at combining state of the art implementations of the Weather Research and Forecasting (WRF) model with multivariate statistical learning techniques to provide the most accurate forecasts of day-ahead hourly irradiance in Singapore. Three implementations of WRF-including WRF-solar-were used to produce three years of hourly day-ahead irradiance forecasts. Their performances were compared with that of the Global Forecasting System (GFS), which was interpolated to provide hourly forecasts. A multivariate post-processing procedure combining Principal Component Analysis (PCA) and stepwise variable selection was developed and applied to the four models. A smart persistence model and a climatological forecast were also implemented and served as benchmarks. The skill of the various models were evaluated using several metrics and statistical tests. We found that WRF-solar combined with our proposed statistical learning method outperformed smart persistence, a climatological forecast and GFS for day-ahead forecasts of irradiance. In particular, our model was shown to have a Root Mean Square Error (RMSE) 23% lower than smart persistence.

[1]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[2]  John S. Kain,et al.  The Kain–Fritsch Convective Parameterization: An Update , 2004 .

[3]  H. Niino,et al.  An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog , 2006 .

[4]  Dazhi Yang,et al.  Day-Ahead Solar Irradiance Forecasting in a Tropical Environment , 2015 .

[5]  Mathieu David,et al.  A Neural Network Post-processing Approach to Improving NWP Solar Radiation Forecasts , 2014 .

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

[7]  S. E. Haupt,et al.  The Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability , 2016 .

[8]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[9]  José A. Ruiz-Arias,et al.  Evaluation of DNI forecast based on the WRF mesoscale atmospheric model for CPV applications , 2012 .

[10]  David Pozo-Vázquez,et al.  Assessing the Surface Solar Radiation Budget in the WRF Model: A Spatiotemporal Analysis of the Bias and Its Causes , 2016 .

[11]  Andreas Kazantzidis,et al.  Evaluation of WRF shortwave radiation parameterizations in predicting Global Horizontal Irradiance in Greece , 2016 .

[12]  Dazhi Yang,et al.  Estimation and Applications of Clear Sky Global Horizontal Irradiance at the Equator , 2014 .

[13]  S. Pelland,et al.  Solar and photovoltaic forecasting through post‐processing of the Global Environmental Multiscale numerical weather prediction model , 2013 .

[14]  Peter Lynch,et al.  The origins of computer weather prediction and climate modeling , 2008, J. Comput. Phys..

[15]  R. Verzijlbergh,et al.  Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications , 2015 .

[16]  Cristina Cornaro,et al.  Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast , 2015 .

[17]  J. A. Ruiz-Arias,et al.  Benchmarking of different approaches to forecast solar irradiance , 2009 .

[18]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[19]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[20]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[21]  James M. Wilczak,et al.  The Accuracy of Solar Irradiance Calculations Used in Mesoscale Numerical Weather Prediction , 2005 .

[22]  John Boland,et al.  Post-processing of solar irradiance forecasts from WRF model at Reunion Island , 2014 .

[23]  Detlev Heinemann,et al.  Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio‐temporal resolutions , 2016 .

[24]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

[25]  Hadrien Verbois,et al.  Probabilistic accumulated irradiance forecast for Singapore using ensemble techniques , 2016, 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC).

[26]  J. Kleissl,et al.  Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States , 2011 .

[27]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Cristina Cornaro,et al.  Model output statistics cascade to improve day ahead solar irradiance forecast , 2015 .

[29]  M. Chin,et al.  Sources and distributions of dust aerosols simulated with the GOCART model , 2001 .

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

[31]  Detlev Heinemann,et al.  Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks , 2016 .

[32]  W. Collins,et al.  Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models , 2008 .

[33]  G. Thompson,et al.  A Study of Aerosol Impacts on Clouds and Precipitation Development in a Large Winter Cyclone , 2014 .

[34]  S. E. Haupt,et al.  WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction , 2016 .

[35]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[36]  Francisco J. Santos-Alamillos,et al.  Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain) , 2012 .

[37]  H. Pedro,et al.  Benefits of solar forecasting for energy imbalance markets , 2016 .