• 대한전기학회논문지:전력기술부문A
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• 제55권4호
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• pp.172-178
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• 2006

In this paper, an advanced demand clustering algorithm which can explore the planned maintenance outage of generators in changed electricity industry is proposed. The major contribution of this paper can be captured in the development of the long-term estimates for the generation availability considering planned maintenance outage. Two conflicting viewpoints, one of which is reliability-focused and the other is economy-focused, are incorporated in the development of estimates of maintenance outage based on the advanced demand clustering algorithm. Based on the advanced clustering algorithm, in each demand cluster, conventional effective outage of generators which conceptually capture maintenance and forced outage of generators, are newly defined in order to properly address the characteristic of the planned maintenance outage in changed electricity markets. First, initial market demand is classified into multiple demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the initial demand. Then, based on the advanced demand clustering algorithm, the planned maintenance outages and corresponding effective outages of generators are reevaluated. Finally, the conventional demand clusters are newly classified in order to reflect the improved effective outages of generation markets. We have found that the revision of the demand clusters can change the number of the initial demand clusters, which cannot be captured in the conventional demand clustering process. Therefore, it can be seen that electricity market situations, which can also be classified into several groups which show similar patterns, can be more accurately clustered. From this the fundamental characteristics of power systems can be more efficiently analyzed, for this advanced classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• 수산해양기술연구
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• 제56권3호
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• pp.265-276
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• 2020

The objective of this study is to develop education programs for cultivating smart aquaculture experts through a education demand survey of industries, high school students, university (graduate) students and field workers. The industry demand analysis was conducted as an in-depth interview on representives from seven companies. Education demand surveys were conducted on 96 students and field workers in the Jeonnam region. Results on the demand survey were analyzed using frequency analysis and cross-analysis. The company representatives responded that they want to participated in internship and retraining programs to proactively secure manpowers with convergence capabilities about smart aquaculture. Seven companies preferred manpowers with basic competencies on ICT (Information and Communications Technologies) or aquaculture. The most respondents in the demand survey want to participate in the education program for experience on advanced technology, self-development and enhancement of work capability. On the other hand, some respondents said that the education is time-consuming and that the education program does not fit their level. Thus, the education program should be developed in a way to minimize the spatial and temporal limitations of education targets and to improve understanding of non-majors by reflecting the demands of human resources in the industrial field.

• 한국철도학회논문집
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• 제17권4호
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• pp.307-312
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• 2014

본 연구는 중앙선의 여객수송수요를 효율적으로 예측하기 위한 방법으로 계절성 요인을 고려한 ARIMA 모형을 제안하였다. 특히, 최근의 관광수요를 반영하기 위하여 2013년 4월 개통되어 운행되고 있는 중부내륙권 관광전용열차(O-train, V-train)의 수요를 포함하여 예측모형을 구축하였다. 이를 위하여 2005년 1월부터 2013년 7월까지의 월별 시계열 데이터(103개)를 사용하여 최적의 모형을 선정하였으며 예측결과 중앙선의 여객 수송수요는 지속적으로 증가할 것으로 나타났다. 구축된 모형은 중앙선의 단기수요를 예측하는데 활용이 가능하다.

• The Journal of Asian Finance, Economics and Business
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• 제8권10호
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• pp.297-303
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• 2021

The performance of the retail industry in a country, which simultaneously reflects the demand for retail space, is significantly influenced by the macroeconomic environment of said country. However, in the case of Malaysia, studies regarding this issue are limited. Therefore, this paper aims to identify the macroeconomic determinants of the demand for retail space in shopping centers in Malaysia through the study of six variables: per capita income, private expenditure, inflation rate, interest rate, total population, and the number of tourists arrival. The nexus between these variables and the demand for retail space in shopping centers were examined by cointegration and causality tests, and regression analysis using quarterly data for the period 1993Q1 to 2016Q4. The results from bivariate cointegration tests indicate that inflation rate, interest rates, population size, and the number of tourists arrival have significant long-run relationships with the demand for retail space of Malaysian shopping centers. Meanwhile, the Granger causality tests show that only population size can cause the demand for shopping centers' retail space. Finally, the results from the regression analysis revealed that income per capita, private expenditure, interest rates, and population are the variables that significantly influence the demand for the retail space of the Malaysian shopping centers.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• 한국수소및신에너지학회논문집
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• 제33권5호
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• 한국의류산업학회지
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• 제24권5호
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• pp.557-566
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• 2022

This study aims to investigate Jinju silk companies, production items, and silk industry supporting projects from 2019 to 2021 in order to discuss the current status of the silk industry. The following are this study's methods: First, a list of Jinju silk companies that have been operating for the past three years (2019-2021) was prepared to investigate the current status of the Jinju silk industry. Second, an investigation was conducted into the representative products produced in Jinju over the past three years; this investigation was conducted using direct interview. Third, an investigation was conducted on the projects that supported the Jinju silk industry over the past three years, and the list of members of the Gyeongnam Textile and Jinju Silk Industry Cooperative Association-a facility of Gyeongsangnam-do Province, the Jinju City Hall brochure (2019), and the SMINFO(SMall business status INFOrmation System) were utilized for this purpose. The following are the results: First, Jinju silk companies are classified into four categories, namely weaving, dyeing, twisting, and designing companies. According to data from 2021, 83% (34 of 41) of silk companies were weavers. Second, the demand for solid fabrics has increased over the past three years. The demand for patterned jacquard fabrics in producing Hanbok and Western-style clothing has decreased. Third, support for the Jinju silk industry could be classified into five categories: support for the operation of silk research institutions, support for the diversification of Jinju silk, support for the promotion of Jinju silk, support for the operation of silk manufacturers, and others.

• 월간포장계
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• 통권148호
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• pp.87-98
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• 2005

• 화약ㆍ발파
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• 제12권1호
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• pp.39-45
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• 1994

• 월간포장계
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• 통권136호
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• pp.94-105
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• 2004