• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.161-163
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• 2006

This paper introduce the technology of peak demand reduction using automatic water tank pumping system on the apartment. That systems on the apartments installed water tank can control pumping(electricity) demand. Generally, system peak demand is occurred at the same time on workday and many water pumps consume electric power randomly. At this point, shift of operating time of water pump can reduce peak demand using automatic water tank pumping system. We were operating this system on some apartments for test of effect of peak demand reduction. and we represent result of demand shift. This result suggests that spread of the automatic water pumping system can contribute to reduce system peak demand and reduce system operation cost.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.106-109
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• 1994

The object of this study is to propose the simplified power demand control system which is appliable to existing buildings or new buildings. Through the technical survey and power demand analysis in office buildings, the electric facilities which can be controlled are selected. Power demand control program can be controlled the electric facilities in order, and displayed the facility operation state. The proposed power demand control system is cost-effective and flexibly adoptable in system upgrade or retrofit.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1296-1304
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• 2013

This study performed an analysis on power demand reduction effects exhibited by demand response programs, which are advanced from traditional demand-side management programs, in the smart grid environment. The target demand response systems for the analysis included incentive-based load control systems (2 month-ahead demand control system, 1~5 days ahead demand control system, and demand bidding system), which are currently implemented in Korea, and price-based demand response systems (mainly critical peak pricing system or real-time pricing system, currently not implemented, but representative demand response systems). Firstly, the status of the above systems at home and abroad was briefly examined. Next, energy saving effects and peak demand reduction effects of implementing the critical peak or real-time pricing systems, which are price-based demand response systems, and the existing incentive-based load control systems were estimated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• 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.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.902-904
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• 1996

In this paper, we proposed the reasonable design standard and countermeasures of Demand Factor for large office buildings, that was made by the statistical way considering actual conditions, such as investicated electric equipment capacity, electric power consumption, etc. So as to save electric equipment investment, the decrease of power loss, the improvement of facilities utilization and the decrease of electric rates, we can be contributed by the application of the design standard. The result of saving effect is showed to confirm the practical use of the proposed Demand Factor, and also, it is believed that this proposed Demand Factor will be useful in electric equipment operation and planning.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.991-994
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• 2016

This paper presents a load management method based on OpenADR of smart grid. Previous demand side algorithm is restricted on reducing peak power. But, in this paper we suggest a method of performing the energy-saving control according to the power price utilizing building automatic control system installed on the customer side in the case of hourly differential pricing signal is transmitted to the open automated demand response system. And, we showed the integrated demand management software for 3 buildings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1225-1228
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• 2011

This paper presents an advanced energy saving algorithm in building. It is important to aggregate a various demand side resource which is controllable on demand response environment. Previous demand side algorithm for building is restricted on peak power. In this paper, we suggest duty cycle algorithm for AHU on demand response to reduce the quantity of building power consumption. The test results show that the proposed algorithm is very effective.

• Journal of Satellite, Information and Communications
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• v.8 no.2
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• pp.56-61
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• 2013

With the rapidly increasing power demand in recent years, variety of methods have been proposed for efficient power consumption.. Among them, the most representative example is demand response system based smart grid. Demand response system is not passive, one-side power demand. This system can efficiently consume through communication between service provider and power consumer. Demand response system uses HTTP based TCP/IP. And currently, there are variety of communication application protocol. In this paper, we analyze procotol type and application for demand response system.