• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.345-347
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• 2005

The electric demands increase, financial need for new power plant constructions and environmental problem have led to search for more efficient energy production and load management. To minimize the construction of power plants and reduce total power consumption include installation of demand controller to industrial applications. Accordingly to maximize the load control by the diffusion of demand controller, govermental economic supports as well as the analysis of energy saving effects. This paper presents the cost-effectiveness analysis for DSM program evaluation and case study to analyze demand controller DSM program.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.511-516
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• 1996

In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.778-780
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• 1996

The predictive maximum demand controllers often bring about large number of control actions during the every integrating period and/or undesirable load-disconnecting operations during the begining stage if the integrating period. To solve these problems, a fuzzy predictive maximum demand control algorithm is proposed, which determines the sensitivity if control action by urgency if the load interrupting action along with the predicted demand reading to the target or the time arriving at the end stage if the integrating period. A prototype controller employing the proposed algorithm also is developed and its performances are tested by PROCOM SYSTEMS Corperation of Korea.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.1
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• pp.29-39
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• 1992

In this paper, the basic idea of optimum electric system design by means of the forecasting of maximum demand is presented, and the load characteristics and practical operating conditions are based on the technical data. After reconstruction of th model plant by use of above method, power supply reliability, future extention, initial cost, and running cost saving effects are analyzed. As a result, it is verified that the systems wherein the power is supply to each load frm main transformer whose capacity is calculated by forecasting are economic rather than the systems wherein the power is supply to each electric feeders from each corresponding transformer.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.1088-1090
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• 1993

This paper shows efficient peak demand control method in office buildings. With a rapid growth of national economics and living standard, electrical energy consumption markedly increased. Expecially, it is increased electrical energy comsumption in the office buildings and thus an energy conservation through efficient use of electricity became more important. From the data of electric equipment capacity and electric power consumption for 96 buildings, current levels of demand factor and a growth trend of peak loads by office buildings were surveyed and analyzed. In addition the efficient peak demand control method in office buildings were studied.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.81-91
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• 2012

The world has been replacing fast fossil fuels vehicles with electric vehicles(EVs) to cope with climate change. The government set a goal which EVs will be substitute at least 10% of the domestic small vehicles with EVs until 2020, and will try to build electric charging infrastructures in apartments with the revision the law of 'the housing construction standards'. In apartments the EVs charging infrastructure and parking space is, essential to accomplish the goal. But the studies on EVs demand are few. In this study, we predicted that the demand for EVs using time-series analysis of statistical data, survey results for apartments residents in the metropolitan area. As a result, the ratio of the EVs appeared to be 6~21% for the total vehicles in a rental apartments for the years 2020, 21~39% in apartments for sales. For the EVs, the maximum power required for 1,000 households in rental apartment is predicted to be about 4200 kwh on a daily basis, while the maximum power in the apartment for sales is predicted to be 7800kwh.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.698-698
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• 2009

화력발전이 많은 비중을 차지하는 전력생산 산업은 온실가스($CO_2$)의 최대 배출 원으로 알려져 있으며 증가하는 전력 수요 뿐 만 아니라 다가오는 기후변화협약에 대응하기 위하여 $CO_2$ 회수 및 공정 개선에 관한 연구가 많이 수행되고 있다. 특히 현재 연구되고 있는 전력분야의 대표적인 $CO_2$ 회수기술은 연소 후 포집(Post-combustion capture), 순산소 연소(Oxy-fuel combustion), 연소전 탈탄소화(Pre-combustion) 3가지로 구분된다. 이중 연소전 탈탄소화 기술은 석탄가스화복합발전(IGCC) 기술과 연계하여 $CO_2$를 회수할 수 있는 방법으로 가스화 된 석탄가스에 Water-Gas Shift 반응과, $CO_2$ 분리로 얻어진 탈 탄소 연료를 통해서 전력을 생산한다. 이 기술의 핵심은 생성된 $CO_2/H_2$ 복합가스로부터 $CO_2$를 분리하는 공정으로 차세대 회수 기술로는 Membrance Reactor, SOFC, Oxygen Ion Transfer Membrane(OTM), 그리고 가스 하이드레이트가 있다. 이중 가스 하이드레이트는 $CO_2$의 회수 뿐 만 아니라 처리 기술에도 적용 가능하지만 우리나라에는 이에 관한 기술이 전무한 형편이다. 본 연구에서는 가스 하이드레이트 형성원리를 이용하여 정온 정압 조건에서 $CO_2/H_2$ 하이드레이트를 제조하였으며 특히, 하이드레이트 형성 촉진제인 THF(Tetrahydrofuran)를 첨가하여 THF 농도에 따른 상평형 및 속도론 실험을 수행 하였다. 이러한 연구는 연소전 탄소화 기술에서의 $CO_2$ 회수 분리에 대한 핵심 연구임과 동시에 탄소배출권 규제에 실질적인 기여를 할 수 있을 것으로 사료된다.