• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Journal of the Korean Solar Energy Society
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• v.40 no.4
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• pp.35-44
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• 2020

Currently, various policies regarding ecofriendly vehicles are being proposed to reduce carbon emissions. In this study, the required areas for charging electric vehicle (EV) batteries using electricity produced by photovoltaic (PV) power plants were estimated. First, approximately 2.4 million battery EVs, which represented 10% of the total number of vehicles, consume approximately 404 GWh. Second, the power required for charging batteries is approximately 0.3 GW, and the site area of the PV power plant is 4.62 ㎢, which accounts for 0.005% of the national territory. Third, from the available sites of buildings based on the region, Jeju alone consumes approximately 0.2%, while the rest of the region requires approximately 0.1%. Fourth, Seoul, which has the smallest available area of mountains and farmlands, utilizes 0.34% of the site for PV power plants, while the other parts of the region use less than 0.1%. The results of this study confirmed that the area of the PV power plant site for producing battery-charging power generated through the supply of EVs is very small. Therefore, it is desirable to analyze and implement more specific plans, such as efficient land use, forest damage minimization, and safe maintenance, to expand renewable energy, including PV power.

• Proceedings of the KIEE Conference
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• summer
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• pp.698-700
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• 2004

Electric power restructuring offers a major change to the vertically integrated utility monopoly. Deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. The energy trading levels between market participants is largely dependent on the short-term price forecasts. This paper presents the short-term System Marginal Price (SMP) forecasting implementation using backpropagation Neural Network in competitive electricity market. Demand and SMP that supplied from Korea Power Exchange (KPX) are used by a input data and then predict SMP. It needs to analysis the input data for accurate prediction.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2283-2286
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• 2003

This paper presents conservation of electrical energy in building with harmonics analysis and compensation which occur in electrical system. We use load controlling and management system in order to adjust load factor of system.The maximum demand limiting and controlling are used ,then the system can acquire the prediction and compare it to the maximum demand set point.The electrical signal analysis based on FFT technique. The harmonics are compensated by using harmonic filters.This system consists computer which works as controller, processor , analysis and database unit together with digital power meter in form of multidrop network through serial communication via RS-485.The load control system uses PLC to control load via serial communication RS-485. The A/D converter is used for sampling the electrical signals via parallel port of computer.The harmonic filters are controlled by a computer.The data of measurement such as voltage, current, power, power factor, total harmonic distortion, energy, etc., can be saved as database and analysis. The load factor is adjusted by limiting and controlling maximum demand. The load factor adjustment can reduce the cost of electric consumption and energy generation together with harmonics compensation in order to increase high efficiency of electrical system.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.210-212
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• 1999

This paper proposes a novel wavelet transform based technique for prediction of System Marginal Price(SMP). In this paper, Daubechies D1(haar), D2, D4 wavelet transforms are adopted to predict SMP and the numerical results reveal that certain wavelet components can effectively be used to identify the SMP characteristics with relation to the system demand in electric power systems. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to predict the SMP on the next scheduling day through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed wavelet transform approach can be used as an attractive and effective means for the SMP forecasting.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.267-278
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• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.393-396
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• 2007

Recently one level higher intelligent switching board than established one by the application of intelligent building and digital system are being constructed. Therefore facility's high efficiency, high degree satisfaction, miniaturaization, standardization through application of communication technology and monitoring and controlling by computer system utilized by web-basis power control system and electric IT are practiced. Especially network must be constructed through unified IBS server that monitors every educational institute's switching boards in real time control system. And I intend to create methods to save energy and raise electricity quality by power demand prediction and remote-controled management and operation. In this thesis I intend to suggest measures of forming unified system through researching educational institute's ways of constructing switching board and status analysis and overcoming technical difficulties in user's side and saving and maintenance expense.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.30 no.3
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• pp.130-142
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• 2018

We use heat pumps with thermal storage system to reduce peak usage of electric power during winters and summers. A heat pump stores thermal energy in a thermal storage tank during the night, to meet load requirements during the day. This system stabilizes the supply and demand of electric power; moreover by utilizing the inexpensive midnight electric power, thus making it cost effective. In this study, we propose a system wherein the thermal storage tank and heat pump are modeled using the TRNSYS, whereas the control simulations are performed by (i) conventional control methods (i.e., thermal storage priority method and heat pump priority method); (ii) region control method, which operates at the optimal part load ratio of the heat pump; (iii) load response control method, which minimizes operating cost responding to load; and (iv) dynamic programming method, which runs the system by following the minimum cost path. We observed that the electricity cost using the region control method, load response control approach, and dynamic programing method was lower compared to using conventional control techniques. According to the annual simulation results, the electricity cost utilizing the load response control method is 43% and 4.4% lower than those obtained by the conventional techniques. We can note that the result related to the power cost was similar to that obtained by the dynamic programming method based on the load prediction. We can, therefore, conclude that the load response control method turned out to be more advantageous when compared to the conventional techniques regarding power consumption and electricity costs.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.