• 천문학회보
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• 제41권1호
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• pp.80.1-80.1
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• 2016

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, and true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model [Lee et al., 2012] which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 2011 to 2014 using this model. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. The forecast probability (y) is linearly changed with the cost/loss ratio (x) in the form of y=ax+b: a=0.88; b=0 (C), a=1.2; b=-0.05(M), a=1.29; b=-0.02(X). We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.536-0.853(C), 0.147-0.334(M), and 0.023-0.072(X). We expect that this study would provide a guideline to determine the probability threshold and the cost/loss ratio for space weather forecast.

• 천문학회보
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• 제40권1호
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• pp.84.2-84.2
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• 2015

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model (Lee et al. 2012) which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 1996 to 2014 using this model. Overall frequencies are 61.08% (C), 22.83% (M), and 5.44% (X). The maximum probabilities computed by the model are 99.9% (C), 89.39% (M), and 25.45% (X), respectively. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. For the critical success index widely used, the probability threshold values for contingency tables are 25% (C), 20% (M), and 4% (X). We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.15-0.83(C), 0.11-0.51(M), and 0.04-0.17(X), also depending on a lifetime of satellite. We expect that this study would provide a guideline to determine the probability threshold for space weather forecast.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2011년도 춘계학술대회 초록집
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• pp.140-140
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• 2011

Korea's RPS, which requires that power generation companies obtain a minimum percentage of their generation by using renewable energy, will take effect in 2012. Based on the first-year law enforcement, generation companies have to satisfy 2% of RPS compliance ratio in 2012. Then, the required RPS compliance ratio will increase up to 10% in 2022. Thus generation companies need to construct power plants that utilize various types of renewable energy sources such as PV and wind power. This work is aimed to analyze the cost of such a renewable power source in terms of capital cost, capacity factor, and fuel cost. We provide the analytical expectation on the renewable power generation cost of 2012 focusing on PV, onshore/offshore wind, fuel cell, and IGCC, which are focused by government policy.

• 한국환경과학회지
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• 제24권7호
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• pp.865-874
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• 2015

This study was conducted to investigate the correlation between the distribution chart and input data of the predicted 3-second gust and damage cost, by using the forecast field and analysis field of Regional Data Assimilation Prediction System (RDAPS) as initial input data of Korea risk assessment model (RAM) developed in the preceding study. In this study the cases of typhoon Rusa which caused occurred great damage to the Korean peninsula was analyzed to assess the suitability of initial input data. As a result, this study has found out that the distribution chart from the forecast field and analysis field predicted from the point where the effect due to the typhoon began had similarity in both 3-second gust and damage cost with the course of time. As a result of examining the correlation, the 3-second gust had over 0.8, and it means that the forecast field and analysis field show similar results. This study has shown that utilizing the forecast field as initial input data of Korea RAM could suit the purpose of pre-disaster prevention.

• 전기학회논문지
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• 제63권11호
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• 산업경영시스템학회지
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• 제37권1호
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• pp.120-126
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• 2014

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

• 경영과학
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• 제26권1호
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2168-2177
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• 2018

The unavoidable forecast error of wind power is one of the biggest obstacles for wind farms to participate in day-ahead electricity market. To mitigate the deviation from forecast, installation of energy storage system (ESS) is considered. An accurate model of wind power forecast error is fundamental for ESS sizing. However, previous study shows that the error distribution has variable kurtosis and fat tails, and insufficient measurement data of wind farms would add to the difficulty of modeling. This paper presents a comprehensive way that makes the use of mixed skewness model (MSM) and copula theory to give a better approximation for the distribution of forecast error, and it remains valid even if the dataset is not so well documented. The model is then used to optimize the ESS power and capacity aiming to pay the minimal extra cost. Results show the effectiveness of the new model for finding the optimal size of ESS and increasing the economic benefit.

• 한국건설관리학회논문집
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• 제4권4호
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• pp.192-200
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• 2003

본 연구는 공공건설 공사에 실적자료에 의한 공사비산정 방법의 전산화 적용을 목적으로 먼저 실적공사비의 정의 및 구조와 현행 실적공사비 운영체계 이해를 통해 전산화의 문제점 및 개선기준을 공사비 산정방법 중심으로 분석 기술하였으며 제안한 기준에 따라 전산화 알고리즘을 제시하였다. 기존 예정공사비 산정방법은 일반적인 공종별 분류에 따른 실적자료조건과 단순히 물가지수, 연면적규모에 따른 시공단가를 적용함으로서 공사비산정의 정확성이 요구되며, 본 연구는 이에 대한 개선 안으로 부위와 부분별 공종분류 방식에 의한 데이터 추출조건으로 실적자료의 적용성과 신뢰성을 높였다. 또한 물량규모에 의한 공사비 변동요인을 물량규모 대비 단가의 확률적 회귀분석으로 예측 추정하여 실행 가능한 적정 공사비 산정방법을 제시하였다. 따라서 본 연구에서는 전산화 적용 방안으로 확률적 예정공사비산정방법을 기준으로 시스템 프로세스를 각 영역별로 구축하고 그에 따른 윈도우 어플리케이션 활용 툴과 개별 전산화에 대한 알고리즘을 구축하였다.

• 한국경영과학회지
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• 제17권3호
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• pp.13-26
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• 1992

Set-up reduction is an important aspect of the Japanese Just-In-Time (JIT) and Zero Inventory (ZI) concepts. In this paper, we first analyze the effects of set-up cost reduction on tatal inventory, average lot size and forecast horizon in the dynamic lot size model. We also examine the various effects of set-up cost reduction in the EOQ model and explain why many Japanese firms try to cut set-up cost and/or set-up time greatly.