In-Gyum Kim

The effect of temperature on the electricity demand: An empirical investigation

This paper attempts to estimate the electricity demand function in Korea with quarterly data of average temperature, GDP and electricity price over the period 2005-2013. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the electricity demand function. The results show that short-run price and income elasticities of the electricity demand are estimated to be -0.569 and 0.631, respectively. They are statistically significant at the 1% level. Moreover, long-run income and price elasticities are estimated to be 1.589 and –1.433, respectively Both of results reveal that the demand for electricity is priceand income-elastic in the long-run. The relationship between electricity consumption and temperature is supported by many of references as a U-shaped relationship, and the base temperature of electricity demand is about 15.2°C. It is shown that power of explanation and goodness-of-fit statistics are improved in the use of the lagged dependent variable model rather than conventional model.

Enhancing the Satisfaction Value of User Group Using Meteorological Forecast Information: Focused on the Precipitation Forecast

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group`s satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

Relationship between Result of Sentiment Analysis and User Satisfaction -The case of Korean Meteorological Administration-

기상청에서 현재 시행되고 있는 만족도 설문조사의 한계를 보완하기 위해 SNS를 통한 감성분석이 활용 될 수 있다. 감성분석은 2011∼2014년 동안 ‘기상청’을 언급한 트위터를 수집하여 나이브 베이즈 방법으로 긍정, 부정, 중립 감성을 분류하였다. 기본적인 나이브 베이즈 방법에 긍정, 부정, 중립의 각 감성에서만 출현한 형태소들로 추가사전을 만들어 감성분석의 정확도를 향상시키는 방법을 제안하였다. 분석결과 기 본적인 나이브 베이즈 방법으로 감성을 분류할 경우 약 75%의 정확도로 학습데이터를 재현한데 반해 추가 사전을 적용할 경우 약 97%의 정확성을 보였다. 추가사전을 활용하여 검증자료의 감성을 분류한 결과 약 75%의 분류 정확도를 보였다. 낮은 분류 정확도는 향후 기상 관련의 다양한 키워드를 포함시켜 학습데이 터 양을 늘려 감성사전의 질을 높임과 동시에 상시적인 사전의 업데이트를 통해 개선될 수 있을 것이다. 한편, 개별 어휘의 사전적 의미에 기반한 감성분석과 달리 문장의 의미에 기반하여 감성을 분류할 경우 부정적 감성 비율의 증가와 만족도 변화 추이를 설명할 수 있을 것으로 보여 향후 설문조사를 보완할 수 있는 좋은 수단으로 SNS를 통한 감성분석이 활용될 수 있을 것으로 사료된다.

Analysis on Expected Profit for the Effective Operation of Social Cooperative -Focusing on the Education Model of the Meteorological Field

사회적협동조합의 효율적 운영을 위한 기대수익 분석 -기상분야 교육모델을 중심으로 Analysis on Expected Profit for the Effective Operation of Social Cooperative –Focusing on the Education Model of the Meteorological Field 김인겸, 김혜민, 안숙희, 이승욱, 김정윤, 이기광 국립기상과학원 연구기획운영과, 단국대학교 경영학부 In-Gyum Kim(kimig@korea.kr), Hyu-Min Kim(hmkim84@korea.kr), Suk-Hee Ahn(ahnsh@korea.kr), Seung-Wook Lee(antcraft@korea.kr), Jeong-Yun Kim(yjk@kma.go.kr), Ki-Kwang Lee(kiklee@dankook.ac.kr)

Analyzing Decision-Making Patterns Based on Uncertain Information in a Supply Chain Environment

With the corporate environment nowadays being surrounded by plenty of information, the sharing of information among businesses through mutual cooperation tops the list of hot issues. Predictions of demands from the customer, business, or consumer by sharing information can affect the inventory and order production system. However, notwithstanding the importance of sharing information, empirical studies on quantitative use of information still remain insufficient in spite of many a discussion now being made on the sharing of information. This paper proposes to examine the ways meteorological information may affect the rises in the achievements of supply chains in distributive businesses, the kind of information that noticeably affects the consumer behavioral patterns in the distributive businesses but rarely perceived as a form of information shared by businesses. This study is based on a model in which meteorological information has been added as the one used to predict demands, after the beer distribution game has been modified to fit the current status, and simulations under an assumptive situation, where decisions are made on a daily basis, were conducted 50 times for a period of 1000 days for the generalization of the results, while at the same time a Duncan Test was conducted to determine the threshold to use the meteorological information that will be most profitable to the retailer, wholesaler, supplier and the supply chain as a whole. Our findings indicate that corporations have thresholds that vary from business to business depending upon the ratio of backlog costs to inventory costs. At the same time, our findings also show that there existed effective thresholds depending upon the ratio of backlog costs to inventory costs for the performance of the overall supply chain.