• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.26-30
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

• Earthquakes and Structures
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• v.6 no.4
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• pp.437-455
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• 2014

Some conventional lateral load patterns for pushover analysis, and proposing a new accurate pattern was investigated in present research. The new proposed load pattern has load distribution according weight and stiffness variation in height and mode shape of structure. The assessment of pushover application with mentioned pattern in X type braced steel frames and steel moment resisting frames, with stiffness and mass variation in height, was studied completely and the obtained results were compared with nonlinear dynamic analysis method (including time history analysis). The methods were compared from standpoints of some basic parameters such as displacement, drift and shape of lateral load pattern. It is concluded that proposed load pattern results are closer to nonlinear dynamic analysis (NDA) compared to other pushover load patterns especially in tall and medium-rise buildings having different stiffness and mass during the height.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1379-1383
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• 2005

Introducing the market into the electricity industry lets the multiple participants get into new competition. These multiple participants of the market need new business strategies for providing value added services to customer. Therefore they need the accurate customer information about the electricity demand. Demand characteristic is the most important one for analyzing customer information. In this study load profile data, which can be collected through the Automatic Meter Reading System, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar demand patterns is developed using the load profile data. As results of classification, customers are separated into several groups. And the representative curves for the groups are generated. The number of groups is automatically generated. And it depends on the threshold value for distance to separate groups. The demand characteristics of the groups are discussed. Also, the compositions of demand contracts and standard industrial classification in each group are presented. It is expected that the classified curves will be used for tariff design, load forecasting, load management and so on. Also it will be a good infrastructure for making a value added service related to electricity.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.9
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• pp.513-520
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• 2004

In the paper, we summarize the result of the experimental research for the overload criteria of domestic distribution transformers considering the electric consumption patterns of customers. For the basic characteristic data of distribution transformer overload, the actual experiments are accomplished. The field data of loads are surveyed from sample transformers for analyzing the consumption pattern of customer load. The load data acquisition devices are equipped, and the algorithm of load pattern classification is applied. In addition to this efforts, various load pattern data. in past are gathered. Then the representative load pattern of each customer type in domestic is extracted. The final results of overload criterions are presented as tabular form through the results of experiments and survey are combined. The field test of the experiment results is peformed using the special manufactured transformers, which can measure both the load and top-oil temperature of transformer. Through this, we verify that the results of field test are similar to the laboratory one and the Proposed overload criteria can be effectively applied to the real system.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.131-138
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• 2001

The performance analysis of an electronic equipment test is very complicate due to the variety o vehicles. In this study, automatic design system for the electronic equipment test has been carried out using the standard load patterns. For the test, standard signal patterns for each item are modeled. The test items can be decided by the user by means of these patterns. Also, engineering software modules are developed and proved to be very efficient for analyzing the test results statistically. Experiments are performed for the test system in the vehicle assembly line. By analyzing the test results, it is found that bad samples can be detected without failure. Also, the engineering software modules provide an analytical tool for the automation of the test process.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.10
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• pp.553-558
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• 2009

Energy demand characteristics of hotel, hospital, and office building are compared to provide guidelines for combining building in community energy system design. The annual, monthly, and daily energy demand patterns for electricity, heating, hot water and cooling are qualitatively compared and important features are delineated based on the energy demand models. Key statistical values such as the mean, the maximum are also provided. Important features of the hourly demand patterns are summarized for weekdays and weekends. Substantial variations in both magnitudes and patterns are observed among the 3 building types and smart grouping or combination of building type and size is essential for a successive energy supply.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.64-67
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• 2009

Vibrational micro-forming of pyramidal shape patterns was conducted for an Al superplastic alloy, Al 5083 and a Zr-based bulk metallic glass, $Zr_{62}Cu_{17}Ni_{13}Al_8$. A vibrational micro-forming system was specially designed for generating vibrational load by combining a PZT actuator with a signal generator. Single crystal Si micro dies with wet-etched pyramidal patterns were used as master dies for vibrational micro-forming. The micro-formed pattern height was increasing with increasing the frequency of the vibrational load. In particular, the vibrationally-microformed pattern height was similar or even higher than the statically-microformed pattern height when the load frequency exceeded about 125 kHz. It was also observed that the crystal grains affect the surface quality of the microformed pattern and the distribution of the pattern height in the die cavity array.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.94-102
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• 2005

This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is $3.23{[\%]}$. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only $1.78{[\%]}$ error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.342-347
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• 2008

A Microsoft $Access^{(R)}$ application that estimates hourly building energy load is developed based on statistical field measurements. Hourly patterns of heating, hot water, cooling, and electricity loads are evaluated for an energy consuming community composed of various types of buildings. Popular building types such as apartments, offices, hotels and accomodations, stores, churches, schools and educational institutes are included in the model. For each type of buildings, hourly patterns for a month are measured and compiled to derive a 24-hour load distributions. Daily sum of heating, hot water, cooling, and electricity loads are also measured for the building types. The annual energy need profiles are generated by combining the 24-hour distribution and 365-day consumption patterns. The annual maximum values of the 8760 hours of a year for each load type serves as a guide for selecting a device capacity. A user-friendly interface that ushers users throughout the whole process is provided.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.3-8
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• 2010

Recently, There have been many attempts to optimize energy usage in buildings and houses using Information Technology(IT) and the typical implementation can be found in Intelligent Building and Zero Energy Building. These kinds of buildings need to forecast the building loads, estimate the capacity requirement for power supply, and decide the capacity of the main transformer of the building. Currently, the capacity of the main transformer has been decided just using typical load estimation method not considering the load characteristics and patterns. In this paper, we propose a new load estimation method considering the load characteristics and patterns of the builiding. The proposed method was applied to actual mail center and verified the feasibility of application to actual design of buildings.