• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.107-108
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• 2012

The study on the strength prediction using Maturity is mainly focused on, but the study on the concrete mixing blast furnace slag powder is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by Maturity function and is to compare and examine the strength prediction of concrete mixing Blast Furnace Slag Power using ACI and Logistic Curve prediction equation. So it is intended that fundamental data are presented for quality management and process management of concrete mixing Blast Furnace Slag Power in the construction field.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1182-1189
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• 1995

Several methods have been developed to predict the creep rupture time of the steam pipes in thermal power plant. However, existing creep life prediction methods give very conservative value at operating stress of power plant and creep rupture strain cannot be well estimated. Therefore, in this study, creep rupture time and strain prediction method accounting for material damage and grain boundary sliding is newly proposed and compared with the existing experimental data. The creep damage evolves by continuous cavity nucleation and constrained cavity growth. The results showed good correlation between the theoretically predicted creep rupture time and the experimental data. And creep rupture strain may be well estimated by using the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.4
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• pp.298-304
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• 2003

In this paper a free-form hull design program and performance prediction program for planing boat is introduced. This program enables the designer to do complex geometric hull shape design on a personal computer and accurately to predict power requirements for a given loading and velocity. For a free form design, Bezier curve model is adopted as a basic representation tool of curves and surfaces, and this program has versatile functions to do fairing jobs with a convenient graphical user interface. After creating a hull form the geometric data is provided in a manner compatible with a variety of analysis tools including 'Motion Analysis(by Zarnick)' for prediction of motion characteristics in regular waves, 'Running Attitude (by Savitsky)' for prediction of the running attitude and required power.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.453-456
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• 2022

The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.520-526
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• 2016

This study aims to develop a distress prediction model, in order to evaluate the distress prediction power for first-class hotels and to calculate the average financial ratio in the Seoul area by using the financial ratios of hotels in 2015. The sample data was collected from 19 first-class hotels in Seoul and the financial ratios extracted from 14 of these 19 hotels. The results show firstly that the seven financial ratios, viz. the current ratio, total borrowings and bonds payable to total assets, interest coverage ratio to operating income, operating income to sales, net income to stockholders' equity, ratio of cash flows from operating activities to sales and total assets turnover, enable the top-level corporations to be discriminated from the failed corporations and, secondly, by using these seven financial ratios, a discriminant function which classifies the corporations into top-level and failed ones is estimated by linear multiple discriminant analysis. The accuracy of prediction of this discriminant capability turned out to be 87.9%. The accuracy of the estimates obtained by discriminant analysis indicates that the distress prediction model's distress prediction power is 78.95%. According to the analysis results, hotel management groups which administrate low level corporations need to focus on the classification of these seven financial ratios. Furthermore, hotel corporations have very different financial structures and failure prediction indicators from other industries. In accordance with this finding, for the development of credit evaluation systems for such hotel corporations, there is a need for systems to be developed that reflect hotel corporations' financial features.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.918-927
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• 2018

Based on the actual operating environment of transformer, the aging tests of nitrile butadiene rubber (NBR) were conducted systematically under four conditions: in air, in transform oil, under compression in air and under compression in transform oil to studythe effect of high temperature, transform oil and compression stress simultaneously on the thermal aging behaviors of nitrile butadiene rubber and predict the lifetime. The effects of liquid media and compression stress simultaneously on the thermal aging behaviors of nitrile butadiene rubber were studied by using characterization methods such as IR spectrosc-opy, thermogravimetric measurements, Differential Scanning Calorimetry (DSC) measurements and mechanical property measurements. The changes in physical properties during the aging process were analyzed and compared. Different aging conditions yielded materials with different properties. Aging at $70^{\circ}C$ under compression stress in oil, the change in elongation at break was lower than that aging in oil, but larger than that aging under compression in air. The compression set or elongation at break as evaluation indexes, 50% as critical value, the lifetime of NBR at $25^{\circ}C$ was predicted and compared. When aging under compression in oil, the prediction lifetime was lower than in air and under compression in air, and in oil. It was clear that when predicting the service lifetime of NBR in oil sealing application, compression and media liquid should be involved simultaneously. Under compression in oil, compression set as the evaluation index, the prediction lifetime of NBR was shorter than that of elongation at break as the evaluation index. For the life prediction of NBR, we should take into account of the performance trends of NBR under actual operating conditions to select the appropriate evaluation index.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.