• 품질경영학회지
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• 제51권4호
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• 통합자연과학논문집
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• 제12권1호
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Management Science and Financial Engineering
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• 제19권1호
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• pp.1-10
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• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.442-443
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• 2022

최근 공장의 트렌드가 아날로그 방식에서 스마트 팩토리로 변화함에따라 스마트 팩토리를 편리하게 이용하는 다양한 기능들이 존재한다. 본 논문은 스마트 팩토리의 기능중 스마트 팩토리 내 수요예측의 다양한 기법을 살펴보고자 한다.

• Structural Engineering and Mechanics
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• 제70권3호
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• pp.289-301
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• 2019

Hysteretic energy is defined as energy dissipated through inelastic deformations during a ground motion by the system. It includes frequency content and duration of ground motion as two remarkable parameters, while these characteristics are not seen in displacement spectrum. Since maximum displacement individually cannot be the appropriate criterion for damage assessment, hysteretic energy has been evaluated in this research as a more comprehensive seismic demand parameter. An innovative methodology has been proposed to establish a new equivalent linear model to estimate hysteretic energy spectrum for bilinear SDOF models under two different sets of earthquake excitations. Error minimization has been defined in the space of equivalent linearization concept, which resulted in equivalent damping and equivalent period as representative parameters of the linear model. Nonlinear regression analysis was carried out for predicting these equivalent parameter as a function of ductility. The results also indicate differences between seismic demand characteristics of far-field and near-field ground motions, which are not identified by most of previous equations presented for predicting seismic energy. The main advantage of the proposed model is its independency on parameters related to earthquake and response characteristics, which has led to more efficiency as well as simplicity. The capability of providing a practical energy based seismic performance evaluation is another outstanding feature of the proposed model.

• 한국빅데이터학회지
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• 제5권1호
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• pp.227-239
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• 2020

본 논문에서는 산업현장에서 통신 오류에도 불구하고 최대전력수요를 예측하는 방법을 소개한다. 최근 국내의 탈원전 정책으로 전력가격상승은 불가피하며, 이에 따른 전력수요 관리를 위한 전력사용량과 최대부하관리는 중요한 문제로 부상하고 있다. 이에 따라, 피크전력을 예측하고 관리하는 것이 중요하다. 하지만 실제 산업현장에서는 각종 설비 및 센서에서 발생하는 노이즈 등으로 인해 측정된 전력데이터의 손실 및 변조 등의 문제가 발생한다. 측정된 유효전력 데이터가 손실된 경우 정확한 값을 예측하기 어렵다. 이 연구는 측정된 유효전력 데이터가 손실될 경우 이상 징후와 결측값을 예측하고 수정하는 모델을 제시한다. 본 연구에 사용된 모델은 산업현장에서 통신 오류가 발생할 경우 최대 전력수요를 예측하는 데 유용할 것으로 예상한다.

• 인터넷정보학회논문지
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• 제21권5호
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• pp.39-48
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• 2020

Recently, the study and development of environment-friendly energy technique have increased in worldwide due to environmental pollution and energy resources problems. In vehicle industry, the development of electric vehicle(EV) is now on progress, and also, many other governments support the study and development and make an effort for EV to become widely available. In addition, though they strive to construct the EV infra such as a charge station for EV, the techniques related to managing charge demand and peak power are not enough. The standard of EV communication has been already established as ISO/IEC 15118, however, most of implemented EVs and EV charge stations do not support any communication between each of them. In this paper, an improved slow charge scheme for non-communication EVs is proposed and designed by using predicting charge demand. The proposed scheme consists of distributed charge model and charge demand prediction. The distributed charge model is designed to manage to distribute charge power depending on available charge power and charge demand. The charge demand prediction is designed to be used in the distributed charge model. The proposed scheme is based on the collected data which were from EV slow charge station in business building during the past 1 year. The system-level simulation results show that the waiting time of EV and the charge fee of the proposed scheme are better than those of the conventional scheme.

• 한국항만경제학회지
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• 제37권1호
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• pp.71-85
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• 2021

기업이 물류비용을 절감할 수 있는 정교한 수요 예측 모형은 그동안 수많은 연구를 통해 다양한 방법들이 제시되었다. 이러한 연구들은 주로 수요 패턴에 의해서 적용 가능한 수요 예측 모형을 결정하고, 통계적 검증을 통해서 모형의 정확성을 판단하였다. 수요 패턴은 크게 규칙성과 불규칙성으로 나뉘어 질 수 있다. 규칙적인 패턴은 주문이 정기적이고 주문량이 일정한 경우를 의미한다. 이러한 경우에는 주로 회귀모형이나 시계열 모형을 통해서 수요를 예측하는 방법들이 사용된다. 그러나 불규칙적이고 주문량의 변동 폭이 큰 경우는 간헐적 수요(Intermittent Demand)라고 하는데, 기존의 회귀 모형이나 시계열 모형으로는 수요 예측의 오류 발생 가능성이 높기 때문이다. 간헐적 수요를 보이는 품목에 대해서는 주로 Croston모형 혹은 Holts모형 등을 사용하여 수요를 예측한다. 본 연구에서는 간헐적 수요 패턴을 보이는 항공 화물의 다양한 품목에 대해서 수요 패턴을 분석하고, 다양한 모형을 통해 수요를 예측하여 각 모형의 예측력을 비교 분석하였다. 이 과정에서 항공 화물의 품목별, 지역별로 다양한 모형의 적합도를 분석하여 항공사가 가장 효율적으로 운영할 수 있는 항공 화물의 수요 예측 모형에 대한 개발 방향을 제시하고자 함이 본 논문의 목적이다.

• International Journal of Concrete Structures and Materials
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• 제10권4호
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• pp.513-525
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• 2016

The resistance-demand approach has emerged as an effective approach for determining the shear capacity of reinforced concrete beams. This approach is based on the fact that both the shear resistance and shear demand are correlated with flexural tensile strain from compatibility and equilibrium requirements. The basic shear strength, under a given loading is determined from the intersection of the demand and resistance curves. This paper verifies the applicability of resistance-demand procedure for predicting the shear capacity of high strength concrete beams without web reinforcement. A total of 18 beams were constructed and tested in four-point bending up to failure. The test variables included the longitudinal reinforcement ratio, the shear span to depth ratio, and the beam depth. The shear capacity of the beams was predicted using the proposed procedure and compared with the experimental values. The results of the comparison showed good prediction capability and can be useful to design practice.

• 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.