• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제5권10호
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• pp.293-302
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• 2016

전기는 생산과 소비가 동시에 이루어지므로 필요한 전력 사용량을 예측하고, 이를 충족시킬 수 있는 충분한 공급능력을 확보해야만 안정적인 전력 공급이 가능하다. 특히, 대학 캠퍼스는 전력 사용이 많은 곳으로 시간과 환경에 따라 전력 변화폭이 다양하다. 이러한 이유로, 효율적인 전력 공급 및 관리를 위해서는 전력 사용량을 실시간으로 예측할 수 있는 모델이 요구된다. 국내외 대학 건물에 대해서는 전력 사용 패턴과 사례 분석을 통해 전력 사용에 영향을 주는 요인들을 파악하기 위한 다양한 연구가 진행되었으나, 전력 사용량의 정량적 예측을 위해서는 더 많은 연구가 필요한 상황이다. 본 논문에서는, 기계 학습 기법을 이용하여 대학 캠퍼스의 전력 사용량 예측 모델을 구성하고 평가한다. 이를 위해, 대학 캠퍼스의 주요 건물 클러스터에 대해 전력 사용량을 15분마다 1년 이상 수집한 데이터 셋을 사용한다. 수집된 전력 사용량 데이터는 수열 형태의 시계열 데이터로 기계 학습 모델에 적용 시 주기성 정보를 반영할 수 없으므로, 2차원 공간의 연속적인 데이터로 증강함으로써 주기성을 반영하였다. 이 데이터와 교육기관의 특성을 반영하기 위한 요일과 공휴일로 구성된 8차원 특성 벡터에 대해 주성분 분석(Principal Component Analysis) 알고리즘을 적용한다. 이어, 인공 신경망(Artificial Neural Network)과 지지 벡터 회귀분석(Support Vector Regression)을 이용하여 전력 사용량 예측 모델을 학습시키고, 5겹 교차검증(5-fold Cross Validation)을 통하여 적용된 기법의 성능을 평가하여, 실제 전력 사용량과 예측 결과를 비교한다.

• 복합신소재구조학회 논문집
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• 제3권2호
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• pp.19-27
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• 2012

Structural insulated panels, which are structurally performed panels consisting of a plastic insulation bonded between two structural panel facings are one of emerging products with a viewpoint of its energy and construction efficiencies. These components are applicable to fabricated wood structures. By now, there are few technical documents regulated structural performance and engineering criteria in domestic market. This study was conducted to suggest fundamental reports such as racking resistance, axial capacity, transverse load capacity, and lintel load capacity for SIPs. Test results showed that maximum load was 44.3kN, allowable load was 14.7kN for racking resistance, and that maximum load was 137.6kN, allowable load was 37.4kN/m for axial compression capacity. For transverse load capacity, test results showed $10.3kN/m^2$ of maximum load, $3.4kN/m^2$ of allowable load. For lintel load capacity for SIPs dependent to lengths, allowable loads were 20.4kN for 600mm long lintel, 23.9kN for 1,200mm long lintel, 19.3kN for 1,800mm long lintel, and 2,400mm long lintel had 14.1kN of allowable load. In the near future, when the allowable load for wall application is established, SIPs is considered to substitute the existent post-and-lintel construction to bearing wall structure.

• 대한원격탐사학회지
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• 제23권5호
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• pp.393-400
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• 2007

We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• 대한전기학회논문지:전력기술부문A
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• 제50권1호
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• pp.20-27
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• 2001

In this paper, we propose a method for estimating the peak load of pole-transformer in summer season considering the degree of cooling load possession in customer. The cooling load of customer is selected as the most reliable parameter of peak load in summer season. The proposed estimation method is restricted to the aspect of load management for pole-transformer. The main concept of proposed method is that the error of peak load estimation using load regression equation reduces with considering the degree of cooling load possession in customer. We propose an index for estimation of cooling load possession in each customer. The proposed index is defined as cooling load possession in customer (CLPC) and obtained from the increment of monthly electric energy. The membership function for deciding the uncertainty of cooling load possession in customer is used. The database of pole-transformer in Korea Electric Power Corporation (KEPCO) is used for case studies. Through the case studies, we verify that the proposed method reduces the error of peak load estimation than the conventional method in domestic.

• 한국해양공학회지
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• 제33권5호
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• pp.447-453
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• 2019

Recently, environmental regulations have been strengthened for SOX, NOX, and CO2, which are ship exhaust gases. In addition, according to the 4th Industrial Revolution, research on autonomous ship technology has become active and interest in electric propulsion systems is increasing. This paper analyzes the power load characteristics of an electric propulsion ship, which is the basic technology for an autonomous ship, in terms of energy management. For the load analysis, data were collected for a 6,800 TEU container ship with a mechanical propulsion system, and the propulsion load was converted to an electric power load and clustered according to the characteristics using a SOM (Self-Organizing Map). As a result of the load analysis, it was confirmed that the load characteristics of the ship could be explained by the operation mode of the ship.

• 전기학회논문지
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• 제64권7호
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• pp.971-977
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• 2015

The electric propulsion system requires more reliability and safety than the conventional propulsion system because any sudden changes of electric system would bring tremendous effects on the ship's safety and propulsion. So it is very important to consider the potential transient effects. This paper discusses one of the worst electric accident. That is, one or two of generators are out of service in normal seagoing condition. And the appropriate measures are simulated in order to prevent the frequency decline that can bring the other generator's tripping. In addition, the relation between the transient effects and the major factors(inertia of generator/motors, governor's drooping characteristic and response speed) are also identified using the ETAP software.

• 전기학회논문지P
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• 제56권1호
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• pp.6-12
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• 2007

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor in pure electric vehicles. A general electric drive train of PEV is conceptually rearrange to major subsystems as electric propulsion, energy source, and auxiliary subsystem and the load torque is modeled by considering the aerodynamic, rolling resistance and grading resistance. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

• Journal of international Conference on Electrical Machines and Systems
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• 제2권1호
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• pp.104-110
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• 2013

In this paper an application of centralized pitch angle controller for fixed speed wind turbines based wind farm to mitigate load frequency fluctuation is presented. Reference signal for the pitch angle of each wind turbine is calculated by using proposed centralized control system based on wind speed information. The wind farm in the model system is connected to a multi machine power system which is composed of 4 synchronous generators and a load. Simulation analyses have been carried out to investigate the performance of the controller using real wind speed data. It is concluded that the load frequency of the system can be controlled smoothly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.163-165
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• 1996

In this paper, we are proposed development of electric energy method and load curve. A daily electric energy is forecasted using artificial neural network. The load curve is obtained by combining forecasted electric energy and typical daily load patterns which are classified using KSOM and Fuzzy system. As a result, we know that we could get more accurate results and easier application than the results from based on the hourly historical data.

• 전기학회논문지
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• 제67권1호
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.