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

Abstract

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.

Area day‐ahead photovoltaic power prediction by just‐in‐time modeling with meso‐scale ensemble prediction system

“…and day‐ahead forecasting of aggregated PV power within a certain region using support vector regression (SVR) by Fonseca et al 8 . As regards forecasting based on MEPS, Takamatsu et al 9 . realized prediction by SVR using GHI forecast by MEPS and multiple elements of MSM; in so doing, they evaluated both large errors and average errors 9 .…”

“…As regards forecasting based on MEPS, Takamatsu et al 9 . realized prediction by SVR using GHI forecast by MEPS and multiple elements of MSM; in so doing, they evaluated both large errors and average errors 9 . In addition, Nohara et al 10 .…”

“…With such approach, inputs derived from a physical model are processed by a blackbox model to obtain required outputs, thus improving accuracy. Examples of PV power prediction by black-box models using MSM include multi-site forecasting using gradient boosted regression tree (GBRT) by Persson et al 7 and day-ahead forecasting of aggregated PV power within a certain region using support vector regression (SVR) by Fonseca et al 8 As regards forecasting based on MEPS, Takamatsu et al 9 realized prediction by SVR using GHI forecast by MEPS and multiple elements of MSM; in so doing, they evaluated both large errors and average errors. 9 In addition, Nohara et al 10 conducted stochastic prediction of area PV power by conversion formulas based on beta distribution using MEPS GHI data.…”

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

“…Several studies applied the EPS output from European Center for Medium-Range Weather Forecasts (ECMWF) to machine learning models; S. Sperati et al adopted NN for the post-processing of the ECMWF EPS forecasts for their photovoltaic power forecasting [49], S. Rasp and S. Lerch also employed NN on ECMWF's EPS forecasts for temperature estimation [50], and L. Massidda and M. Marrocu used QR with Gradient-Boosted Regression Trees (GBRT) to each EPS member and combined their output with Integrated Forecast System (IFS) data [51]. In Japan, the MEPS data, which is based on the MSM data from JMA was used, and several studies were reported, including the study of confidence interval estimation using the Just-in-Time model [52] and the study of postprocessing methods for EPS data based on support vector machines [53]. These studies show that EPS data helps to improve the accuracy of PV forecasts.…”

“…These studies show that EPS data helps to improve the accuracy of PV forecasts. In particular, our recent article [53] suggests that the maximum (minimum) forecast error can be suppressed by the EPS output as an explanatory variable in the SVR.…”