• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2003.11a
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• pp.77-79
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• 2003

For the management of maximum demand power, power control system that is consist of CCMS(Central Control and Management System) and MCCS(Minimum Cost Control and management Software) is proposed. MCCS has the basic functions of the set of target power and the enrollment of load control logic. And also MCCS give the simulation of Power rate that help more effective Demand Control.

• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.129-133
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• 2004

Studies on the optimal location of retail store have been made in case of no obstacle(Minagawa etal. 1999). This paper deals with the location problem of retail store considering obstacles (e.g. rivers, railways, highways, etc.) and obstacle-overcoming points (e.g. bridges, railway crossings, zebra crossings, overpasses, etc.). We assume that (1) commercial goods dealt here are typically convenience goods, (2) the population is granted as potential demand, (3) the apparent demand is a function of the maximum migration length and the distance from the store to customers, (4) the scale of a store is same in every place and (5) there is no competitor. First, we construct the basic model of customers' behavior considering obstacles and obstacle-overcoming points. Analyzing the two dimensional model, the arbitrary force attracting customers is represented as a height of a cone where the retail store is located on the center. Second, we formulate the total demand of customers and determine the optimal location that maximizes the total demand. Finally, the properties of the optimal location are investigated by simulation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.10
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• pp.553-558
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• 2009

Energy demand characteristics of hotel, hospital, and office building are compared to provide guidelines for combining building in community energy system design. The annual, monthly, and daily energy demand patterns for electricity, heating, hot water and cooling are qualitatively compared and important features are delineated based on the energy demand models. Key statistical values such as the mean, the maximum are also provided. Important features of the hourly demand patterns are summarized for weekdays and weekends. Substantial variations in both magnitudes and patterns are observed among the 3 building types and smart grouping or combination of building type and size is essential for a successive energy supply.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.59-72
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• 1994

This paper deals with ordering policies of consumable goods which have large demand rates in a multi-level distribution system. The system we are concerned consists of one Central Distribution Center(CDC) and N non-identical Regional Distribution Centers(RDCs) which have different demand rates, minimum fillrates, leadtimes, etc. The customer demand on the RDC is stationary poisson and the RDCs demand on the CDC is superposition of Q-stage Erlang distributions. We approximate the RDCs and CDC demand distribution to nomal in order to enhance the efficiency of algorithm. The relevant costs include a fixed ordering cost and inventory holding cost, and backorder cost. The objective is to find a continuous-review ordering policy that minimizes the expected average costs under constraints of minimum fill rates of RDCs and maximum allowable mean delay of CDC. We developed an algorithm for determining the optimal ordering policies of the CDC and the RDCs. We verified and compared the performance of the algorithm through the simulation using the algorithm result as the input parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1128-1134
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• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.

• Earthquakes and Structures
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• v.14 no.3
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• pp.203-214
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• 2018

Predictive demand and collapse fragility functions are two essential components of the probabilistic seismic demand analysis that are commonly developed based on statistics with enormous, costly and time consuming data gathering. Although this approach might be justified for research purposes, it is not appealing for practical applications because of its computational cost. Thus, in this paper, Bayesian regression-based demand and collapse models are proposed to eliminate the need of time-consuming analyses. The demand model developed in the form of linear equation predicts overall maximum inter-story drift of the lowto mid-rise regular steel moment resisting frames (SMRFs), while the collapse model mathematically expressed by lognormal cumulative distribution function provides collapse occurrence probability for a given spectral acceleration at the fundamental period of the structure. Next, as an application, the proposed demand and collapse functions are implemented in a seismic fragility analysis to develop fragility and consequently seismic demand curves of three example buildings. The accuracy provided by utilization of the proposed models, with considering computation reduction, are compared with those directly obtained from Incremental Dynamic analysis, which is a computer-intensive procedure.

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.

• JOURNAL OF ELECTRICAL WORLD
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• no.1 s.145
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• pp.39-43
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• 1989

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.901-922
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• 2007

To see how the electricity demand for air-conditioning responds to weather condition and what kind of weather condition works better in forecasting maximum daily electricity demand, four different regression models, which are linear, exponential, power and S-curve, are adopted. The regression outcome turns out that the electricity demand for air-conditioning is inclined to rely on the exponential model. Another major discovery of this study is that the electricity demand for air-conditioning responds more sensitively to the weather condition year after year along with the higher non-air-conditioning electricity demand. In addition, it has also been found that the discomfort index explains the electricity demand for air-conditioning better than the highest temperature.