Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%

Identification of 45 New Neutron-Rich Isotopes Produced by In-Flight Fission of a 238U Beam at 345 MeV/nucleon

A search for new isotopes using in-flight fission of a 345 MeV/nucleon 238U beam has been carried out at the RI Beam Factory at the RIKEN Nishina Center. Fission fragments were analyzed and identified by using the superconducting in-flight separator BigRIPS. We observed 45 new neutron-rich isotopes: 71Mn, 73,74Fe, 76Co, 79Ni, 81,82Cu, 84,85Zn, 87Ga, 90Ge, 95Se, 98Br, 101Kr, 103Rb, 106,107Sr, 108,109Y, 111,112Zr, 114,115Nb, 115,116,117Mo, 119,120Tc, 121,122,123,124Ru, 123,124,125,126Rh, 127,128Pd, 133Cd, 138Sn, 140Sb, 143Te, 145I, 148Xe, and 152Ba

From Duty to Right: The Role of Public Education in the Transition to Aging Societies

This paper argues that the introduction of compulsory schooling in early industrialization promoted the growth process that eventually led to a vicious cycle of population aging and negative pressure on education policy. In the early phases of industrialization, public education was undesirable for the young poor who relied on child labor. Compulsory schooling therefore discouraged childbirth, while the accompanying industrialization stimulated their demand for education. The subsequent rise in the share of the old population, however, limited government resources for education, placing heavier financial burdens on the young. This induced further fertility decline and population aging, and the resulting cycle may have delayed the growth of advanced economies in the last few decades

Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model

To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future

The Formation Mechanism of a Thick Cloud Band over the Northern Part of the Sea of Japan during Cold Air Outbreaks

During cold-air outbreaks in winter, a thick cloud band frequently appears over the northern Sea of Japan and produces localized heavy snowfall in the western coastal region of Hokkaido Island, northern part of Japan. The formation mechanism of this thick cloud band is investigated through a series of nonhydrostatic numerical simulations with a horizontal grid spacing of 5 km. The control simulation well reproduces the characteristics of an observed cloud band. The cloud band forms between relatively warm north-northwesterly winds on the northeast side and relatively cold northwesterly winds on the southwest side. Sensitivity experiments in which upstream topography is modified indicate that the formation and intensification of the cloud band depend on the following two effects; one is the effect of a specific mountain located near the coastline in the middle part of Russia's Sikhote-Alin mountain range (SAMR), and the other is the effect of large-scale topography along the SAMR on synoptic-scale low-level cold northwesterlies. The specific mountain deflects the cold airflow and immediately a convergence zone forms downstream of the specific mountain, where the cloud band is initiated. On the northeastern side of this mountain, the Froude number is estimated to be about 0.4 from relatively high topography (～1.2 km), stable stratification (～0.02 s^[-1]), and synoptic-scale wind speed of 10 m s^[-1]. Thus, the relatively high topography strongly blocks a low-level cold air, whereas an upper air with high potential temperature flows downward over the sea. In contrast, on the southwestern side of the mountain, a low-level cold air can pass over the topography, because the Froude number is estimated to be about 1.6 from relatively low topography (～0.8 km) and weak stable stratification (～0.008 s^[-1]). These two airs with different potential temperature create a mesoscale frontal zone over the sea, which causes the further development of the thick cloud band initiated by the coastal specific mountain in the SAMR

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

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation

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

Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation