• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1608-1614
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• 2014

Power demand forecasting is an important factor of the peak management. This paper deals with the 15 minutes ahead load forecasting problem in a DC urban railway system. Since supplied power lines to trains are connected with parallel, the load characteristics are too complex and highly non-linear. The main idea of the proposed method for the 15 minutes ahead prediction is to use the daily load similarity accounting for the load nonlinearity. An Euclidean norm with weighted factors including loads of the neighbor substation is used for the similar load selection. The prediction value is determinated by the sum of the similar load and the correction value. The correction has applied the neural network model. The feasibility of the proposed method is exemplified through some simulations applied to the actual load data of Incheon subway system.

• 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 Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.207-210
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• 2014

This paper is concerned with the prediction of lift-off acoustic loads for a launch vehicle. Intense acoustic load is generated when a launch vehicle is lifted off, and it can induce vibrations of a launch vehicle which cause damage or malfunction of a launch vehicle and a satellite. Lift-off acoustic loads of NARO are predicted by the modified Eldred's second method and the result is compared with the measured data in flight test. The prediction shows similar peak and shape of spectrum to the test data, but some discrepancy can be observed due to the predicted margin. In order to reduce such discrepancy, the sound pressure levels with four source distribution assumptions are calculated. Also, the surface diffraction effects are considered in the predict ion of lift-off acoustic loads, and the predicted result is more similar to the test data.

• 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).

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.4
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• pp.265-272
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• 2011

The electrical load profiles of end-users must be analysed properly to introduce distributed generation system efficiently. In this study, numerical simulation for predicting a residential electrical load profile was developed to satisfy categorized electricity consumption range. We applied bottom-up approach to compose electrical load profile by using data from official reports and statistics. The electrical load profile produced from the simulation predicted peak times of public report accurately and agreed well with the standard residential electrical load profile of official reports within average error of 16.2%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.5
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• pp.94-99
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In addition, algorithm that uses TOU(Time of Use) based on the revelation by the power electric charge which covers most simply is an inefficient operation because it is only for the purpose of reducing the peak power. In this paper, we introduced a non-prediction algorithm with a conventional TOU in order to solve this problem operating the energy storage system economic and efficient.

• Journal of the Korean Solar Energy Society
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• v.33 no.3
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• pp.9-16
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• 2013

In this research, the effect of a heating system, which is powered by direct solar energy accumulated in phase change material (PCM) as heat storage material installed on the floor surface, on the cooling load was studied. Cooling load of a test building designed for this research was measured with fan coil unit and factors affecting it were also estimated. Experiments were performed with and without PCM installed on the building floor to understand the effect of the PCM on the cooling load. Additionally, to confirm the experiments results, the prediction calculation formula by average outside temperature and integrated solar radiation was composed using multivariate regression model. The results suggested that the heating system with PCM on the floor surface has the potential to shift electric power peak by radiating heat, stored during the daytime in it, at night, not increasing the total cooling load much.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.6
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• pp.1465-1473
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• 1994

This paper describes the development strategy and contents of a fatigue life evaluation system, FLEVA. The system is composed of 4 parts; material properties, load histories, cycle counting and life prediction. The cycle counting is based on the rain-flow counting method and peak counting method, and the life prediction is performed based on the linear damage rule. Material properties(static, fatigue) are also provided as a database obtained by a computer aided test system. Case study is performed to verify the developed program.

• Journal of the Korean Geotechnical Society
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• v.26 no.8
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• pp.59-66
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• 2010

In this study, an applicability by using the FEM was investigated for the prediction of both the depth of improvement and the vibration effect when dynamic compaction method is applied. The region was modelled by the field conditions applying dynamic compaction method and the rigid body force was applied to the dynamic load model. Predicted depth of improvement calculated by the vertical peak particle acceleration was compared and analyzed with an existing empirical equation, and the effect of groundwave by deducing the peak particle velocity from vibration sources was compared and analyzed with the results of another existing empirical equation. The results showed that the prediction of the depth of improvement has similar tendency to practice, and the vibration effect has some differences in a particular section from existing equation, but it could predict the safety distance to some degree. The analyzed results are expected to be basic data for the development of reliability of dynamic compaction design with existing empirical method.