• Journal of Information Technology Applications and Management
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• 제26권2호
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• pp.61-73
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• 2019

In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.

• 대한산업공학회지
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• 제14권1호
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• pp.103-117
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• 1988

Replacement demand plays an important role to forecast the total demand of durable goods, while most of the diffusion models deal with only adoption data, namely initial purchase demand. This paper presents replacement demand forecasting models incorporating repurchase rate, multi-ownership, and dynamic product life to complement the existing diffusion models. The performance of replacement demand forecasting models are analyzed and practical guidelines for the application of the models are suggested when life distribution data or adoption data are not available.

• 한국시스템다이내믹스연구
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• 제7권1호
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• pp.67-90
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• 2006

A series of system dynamics model was developed for forecasting demand and supply of human resource in the electricity industry. To forecast demand of human resource in the electric power industry, BLS (Bureau of Labor Statistics) methodology was used. To forecast supply of human resource in the electric power industry, forecasting on the population of our country and the number of students in the department of electrical engineering were performed. After performing computer simulation with developed system dynamics model, it is discovered that the shortage of human resource in the electric power industry will be 3,000 persons per year from 2006 to 2015, and more than a double of current budget is required to overcome this shortage of human resource.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.952-955
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• 2005

In the early 90's, we had serious shortage of construction engineers in Korea. The shortage was acute especially in construction quality control and supervision area, which were gaining social attention due to the road bridge and the department store collapse that took the hundreds of lives in the early 90's in Seoul, Korea. In order to meet the high demand of construction engineers, the engineering license regulations were changed in 1995. Engineers who did not pass the written exam but have equivalent working experience are given engineering license to practice engineering legally. Since year 2000, while the severe engineer-shortage has been resolved, the opposite situation has occurred: there is serious over-supply of construction engineers. Policy makers and engineering practitioners are agreed to bring back the old-fashioned written exam engineer licensing system like before 1995, i.e., no more written exam exemption. However, the engineers who obtained license without taking written exam may not want to go back to old policy which would take their license. It is required to provide appropriate grace period before the new policy takes effect to minimize the impact of the changes. This paper forecasts the supply-demand of construction engineers providing the basis for the most appropriate policy changes.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.833-836
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• 2012

최대수요전력 관리 장치는 현재 사용전력을 모니터링하여 예측 전력을 계산해 수용가에서 설정한 목표전력을 초과하지 않게 부하를 제어하는 장치이다. 부하의 제어는 예측된 최대수요전력이 목표전력을 초과할 경우 경보를 발생하고 부하를 차단하는 방식을 사용하기 때문에 최대수요전력에 대한 정확한 예측이 중요하다. 전력 변동이 심한 수용가에서는 기존의 예측 방법을 사용할 경우 최대수요전력 관리가 안정적이지 못하다는 단점이 있다. 본 논문에서는 기존의 최대수요전력 예측 방법 및 지수평활방법을 살펴보고 칼만 필터를 사용한 예측 방법을 제안한다.

• Nuclear Engineering and Technology
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• 제52권6호
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• pp.1334-1339
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• 2020

This paper presents an empirical and comprehensive forecasting analysis of the uranium price. Prices are generally difficult to forecast, and the uranium price is not an exception because it is affected by many external factors, apart from imbalances between demand and supply. Therefore, a systematic analysis of multiple forecasting methods and combinations of them along repeated forecast origins is a way of discerning which method is most suitable. Results suggest that i) some sophisticated methods do not improve upon the Naïve's (horizontal) forecast and ii) Unobserved Components methods are the most powerful, although the gain in accuracy is not big. These two facts together imply that uranium prices are undoubtedly subject to many uncertainties.

• 문화기술의 융합
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• 제6권3호
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• pp.45-55
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• 2020

최근 우리나라에서 미세먼지에 대한 인식이 확대되면서 다양한 대책과 대응전략이 제시되고 있다. 본 연구는 미세먼지의 발생에 따른 집객공간의 변화를 분석하고 활동 패턴을 찾아내기 위해 국내외 미세먼지 프리존의 현황을 조사하고 각 사례에 따른 미세먼지 관련 제품과 공간의 한계점을 비교 분석하여 그 수요를 전망하고 시사점을 찾아 보고자 한다. 본 연구의 목적은 국내외의 미세먼지 대책과 국내 관련 산업의 현황파악을 중심으로 수요를 전망하는 것이다. 4차 산업 혁명시대에 스마트 시티에 대한 경쟁력을 확보하기 위해서 도심 공간의 위협이 되고 있는 미세먼지를 대응할 전략적 방안을 제시하기 위한 연구방법을 제안한다.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2006년도 정기학술발표대회 논문집
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• pp.453-457
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• 2006

1963년 우수 기술인력의 확보를 위해 기술사제도를 도입한 후, 주택건설 200만호 건설 등 건설경기의 활성화로 인한 건설기술자의 수급불균형 등을 해결하기 위하여 학력 및 경력기술자를 건설기술자로 인정하는 제도를 1995년에 도입하였다. 이러한 인정기술자제도의 도입으로 건설기술자는 양적으로 크게 증가하여 2000년 이후 건설기술자의 심각한 공급초과가 발생하였고, 이에 따라 최고 기술자격인 기술사의 활용에 있어 심각한 폐해가 일어나고 있다. 정부는 최근 WTO협약에 의한 기술개방, 국가간 상호인증 등과 관련한 기술사의 전문성을 확보할 수 있는 법 ${\cdot}$ 제도적 기반을 마련하고 있다. 이러한 제도적 측면을 반영한, 정확한 기술사의 인력수급예측을 통하여 기술사 인력 수급의 문제점을 분석하고, 향후 기술사 인력의 수급을 조정하기 위해 제도적인 측면의 개선방안을 제시하고자 한다. 본 연구의 결과는 향후 선진수준의 기술사 인력수급체계 구축은 물론 효율적이고 일관된 기술사 양성 및 활용 체제 구축에 이용될 수 있을 것이다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.