• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.165-168
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• 1995

The recent rapid growth of air conditioning load has become a major reason of peak load increase in summer. In connection with this, we surveyed the load management projects of utilities world-wide and their detailed activities. This study is to develop a remote load control system using computer and radio communications. We finished the field-test of this system on August 1995 in Seoul area. During the field-test, the remote load control of air conditioners was proved to be well-timed. Two control modes, group control and all control, are available for the user to select. The transmission reliability of the load control signal was very good and the functions of system hardware as well as the software were excellent. So we confirmed the applicability of the load control system including the paper communication network. In this paper, detailed information on the system functions and experimental results are described.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.929-931
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• 1998

This paper describes the peak load forecasting technique of distribution transformers with correlation equation. While customers are demanding safe energy supply, conventional correlation equation that is used for load management of distribution transformers in domestic has some problems. To get accurate correlation equation, se-correlation equation were examined using new collected using the measuring instrument dev for this study. It was recognized that the qua equation was the most accurate for peak forecasting from working electrical energy.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.379-382
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• 2008

This paper gives the support method of DSM program(power load leveling for heating and cooling facilities on building). As the national power peak load increases recently, the peak load reduction is needed. So we studied about remote controlling of power load from heating/cooling facilities on building during peak times. To adopt new DSM program, it is very important to design DSM customer supporting system. So in this paper, we dealt with the result of customer survey, and the DSM potential regarding heating/cooling facilities on building. In conclusion, the peak reduction program of heating/cooling facilities is very important and the incentive of customer should be consist of two incentive types as an installation and power reduction.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Journal of the Korean Solar Energy Society
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• v.33 no.3
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• pp.67-74
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• 2013

These days, although thermal energy is decreasing, electric energy is increasing in building. Also, it is very important to research and distribute BIPV(Building Integrated photovoltaic) because our society consider electricity more significant than other energy in building. Therefore, in this paper, our research team analyzed difference between BIPV yield and building energy consumption through experimental research. As a result, yearly building energy consumption was 104,602.4kWh and BIPV yield was 105,267kWh. And then, totally counterbalanced time took up 26%, reduced electric load time took up 16%. In other words, peak load could be reduced up to 42% by BIPV. As a result, yearly building energy consumption was 104,602.4kWh and BIPV yield was 105,267kWh. And then, totally counterbalanced time took up 26%, reduced electric load time took up 16%. In other words, peak load could be reduced up to 42% by BIPV.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.6
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• pp.40-44
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• 2014

In this paper, maximum power is the lowering device using the facility's energy use and peak load electricity through analyzing attitude should like to make it reduce its power base rate. Simulator to manage the demand for power, a maximum electric power base power from electronic watt-hour meters by a device's signal, predictive power, the current power by computing the goal of power for less than Maximum peak power and peak shift, so that you can manage, and peak York, which role you want a cut Metal heat treatment result which analyzes the data, demand for electricity company over the years of analyzing the characteristics of each load, and effects and Reducing power consumption device every month identified seven Sequence control to the load system and successful power control is about showing that the defined goals.