Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.

[1]  Stephan Rasp,et al.  Neural networks for post-processing ensemble weather forecasts , 2018, Monthly Weather Review.

[2]  Constantin Junk,et al.  Analog-Based Ensemble Model Output Statistics , 2015 .

[3]  Eugenia Kalnay,et al.  Ensemble Forecasting at NMC: The Generation of Perturbations , 1993 .

[4]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien [R package e1071 version 1.7-4] , 2020 .

[5]  E. Epstein,et al.  Stochastic dynamic prediction , 1969 .

[6]  Yu Fujimoto,et al.  Estimation Prediction Interval of Solar Irradiance Based on Just‐in‐Time Modeling for Photovoltaic Output Prediction , 2016 .

[7]  Takashi Oozeki,et al.  Regional and seasonal characteristics of global horizontal irradiance forecasts obtained from the Japan Meteorological Agency mesoscale model , 2015 .

[8]  M. Kunii,et al.  The regional model‐based Mesoscale Ensemble Prediction System, MEPS, at the Japan Meteorological Agency , 2020, Quarterly Journal of the Royal Meteorological Society.

[9]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

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

[11]  C. Bohren,et al.  An introduction to atmospheric radiation , 1981 .

[12]  Qifa Xu,et al.  Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression , 2019 .

[13]  K. Droegemeier,et al.  Objective Verification of the SAMEX ’98 Ensemble Forecasts , 2001 .

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

[15]  Kody M. Powell,et al.  Process intensification of solar thermal power using hybridization, flexible heat integration, and real-time optimization , 2019, Chemical Engineering and Processing - Process Intensification.

[16]  Ken Nagasaka,et al.  Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting , 2009, J. Adv. Comput. Intell. Intell. Informatics.

[17]  Joao Gari da Silva Fonseca,et al.  Analysis of Different Techniques to Set Support Vector Regression to Forecast Insolation in Tsukuba, Japan , 2013 .

[18]  L. D. Monache,et al.  An analog ensemble for short-term probabilistic solar power forecast , 2015 .

[19]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[20]  Hideaki Ohtake,et al.  A diagnostic for advance detection of forecast busts of regional surface solar radiation using multi-center grand ensemble forecasts , 2018 .

[21]  C. Leith.  Theoretical Skill of Monte Carlo Forecasts , 1974 .

[22]  L. D. Monache,et al.  An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting , 2016 .

[23]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[24]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[25]  Joao Gari da Silva Fonseca Junior,et al.  Forecasting Regional Photovoltaic Power Generation - A Comparison of Strategies to Obtain One-Day-Ahead Data , 2014 .

[26]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[27]  Susumu Shimada,et al.  Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model , 2016 .

[28]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[29]  Henrik Ohlsson,et al.  A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014 , 2016 .

[30]  Joao Gari da Silva Fonseca Junior,et al.  On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation , 2015 .

[31]  C. Coimbra,et al.  Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .

[32]  Luca Massidda,et al.  Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting , 2018, Energies.

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[34]  Takashi Oozeki,et al.  Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan , 2013 .

[35]  R. Koenker,et al.  Regression Quantiles , 2007 .

[36]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[37]  Neill E. Bowler,et al.  The MOGREPS short‐range ensemble prediction system , 2008 .

[38]  Cyril Voyant,et al.  Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation , 2012, ArXiv.

[39]  O. Perpiñán,et al.  PV power forecast using a nonparametric PV model , 2015 .

[40]  Z. B. Bouallègue,et al.  Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries , 2011 .