• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.537-546
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• 2019

In this paper, a system that integrates a home concentrate unit(HCU) in a smart electricity meter to collects water, gas, hot water, and heating usage required for AMI has been proposed. The collected data could be transmitted to the in house display(IHD) and server to provide a more economical AMI system. The developed system is less expensive in the network configuration than the existing system, which could reduce the operating cost, and be easy to install. By applying the developed system, the usage of electricity, water, gas, hot water and heating could be measured and these make it easier to apply AMI system. The main contents of the development are the smart electricity meter and embedding of HCU into the smart electricity meter, and transferring these data to IHD and server to structure the AMI system. The each developed unit has been networked to structure the AMI system to perform the actual meter reading operation and show the result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2080-2085
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• 2008

KEPCO's AMR (Automatic Meter Reading) is a system that performs the real-time inspection and management of the 15-minute load profile of electric power consumption through a wired and/or wireless network such as CDMA. It has been utilized widely for real-time collection and data analysis. So far, KEPCO has focused on establishing wireless networks using CDMA and collecting data in real time but failed to consider sufficiently performances that can improve the quality of the original data required in terms of data utilization as well as establish the summary information. In this paper, we are going to show the functions that improve data quality by recording the final renewal time of any erroneous data and maintaining such data lists to use them in the rebuilding of summary information. The goals are to reduce any load applied mainly on the DBMS (Database Management System) of AMR, to enable the real-time performance of establishment in the summary information, and to obtain high-quality inspection data. The performance evaluation result has revealed a 10-fold improvement compared to the traditional disk-based DBMS system when the summary information is established.

• 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.

• Environmental and Resource Economics Review
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• v.25 no.2
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• pp.141-178
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• 2016

This paper, using AMR (Automatic Meter Reading) electricity data accurately measured in real time, analyses the characteristics and patterns of temperature effect on the industrial electricity usage. For this goal, the paper constructs and estimates a model which captures the properties of AMR time series including long-term trends, mid-term temperature effects, and short-term special day effects. Based on the estimated temperature response function and the temperature effect, we categorize the whole industry into two groups: one group with sharp temperature effect and the other with weak temperature effect. Furthermore, the industry group with sharp temperature effect is classified into a summer peak industry group and a winter peak industry group, based on the estimates of the temperature response function. These empirical results carry practical policy implications on the real time electricity demand management.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.261-263
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• 2006

전력 계통은 발전설비, 송전설비, 변전설비, 배전설비 등으로 크게 나눌 수 있다. 전력계통을 구성하는 이런 설비들을 원격 감시하고 제어하는 시스템을 전력자동화 시스템이라고 한다. 이런 전력자동화 시스템은 국내 전체 에너지의 수요, 공급, 수송을 제어하는 에너지 관리시스템(EMS : Energy Management System), 송전 및 변전설비의 감시제어를 담당하는 송변전소 감시제어 시스템(SCADA : Supervisory Control And Data Acquisition), 배전계통의 실비관리 및 운영을 담당하는 배전자동화시스템(DAS : Distribution Automation System), 수용가의 계량기를 원격으로 읽어오는 원격검침시스템 (AMR : Automatic Meter Reading) 통으로 구성되어 있다. 국내에서 운용중인 배전자동화 시스템은 배전선로에 설치되어 있는 개폐기만을 원격 감시 및 제어 하는 보편적인 시스템이다. 한편, 2005년도 전력IT 국가 전략과제로 수행하고 있는 배전지능화 시스템 개발 과제에서는 변전소부터 배전계통과 수용가까지의 모든 전력 설비에 대한 원격감시제어가 가능한 시스템을 배전지능화 시스템이라고 정의하고 이의 개발을 추진하고 있다. 본 논문에서는 ABB, SIEMENS, GE와 같은 국외의 배전 자동화 시스템들을 소개하고, 배전지능화 시스템에서 목표로 하고 있는 GIS 기반 위에서 고 저압 배전설비를 관리하고, SCADA, DAS, GIS(Geographic Information System), AMR, TCS(Trouble Call System) 등 배전지능화 시스템의 중앙제어장치가 갖추어야 하는 주요 기능들에 대해 기술하고자 한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5B
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• pp.511-516
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• 2011

In this paper, we present the AMI System mentioned as one of the main application areas in the M2M. To enable the AMI, it is essentially required to install, deploy and manage all the devices. Especially, Device Management technology is considered to be suitable for these AMI due to its advantages like, easy installation, remote FW/SW upgradeability. We investigate the fundamental operation of the DM and the design of the DM in the AMI is presented.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.281-287
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• 2012

The customer load profile clustering method is used to make the TDLP (Typical Daily Load Profile) to estimate the quarter hourly load profile of non-AMR (Automatic Meter Reading) customers. This study examines how the repeated clustering method improves the ability to discriminate among the TDLPs of each cluster. The k-means algorithm is a well-known clustering technology in data mining. Repeated clustering groups the cluster into sub-clusters with the k-means algorithm and chooses the sub-cluster that has the maximum average error and repeats clustering until the final cluster count is satisfied.

• Korean Journal of Remote Sensing
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• v.23 no.5
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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.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.737-747
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• 2012

Recently, in order to support automatic meter reading for conventional metering devices, an image processing-based approach of recognizing the number meter data in the captured meter images has attracted many researchers' interests. Numerical character segmentation is a very critical process for successful recognition. In this paper, we propose an efficient numeric character segmentation method which can segment numeric characters well for any metering device types under diverse illumination environments. The proposed method consists of two consecutive stages; detection of number area containing all numbers as a tight ROI(Region of Interest) and segmentation of numerical characters in the ROI. Detection of tight ROI is achieved in two steps: extraction of rough ROI by utilizing horizontal line segments after illumination enhancement preprocessing, and making the rough ROI more tight through clipping utilizing vertical and horizontal projection about binarized ROI. Numerical character segmentation in the detected ROI is stably achieved in two processes of 'vertical segmentation of each number region' and 'number segmentation in the each vertical segmented number region'. Through the experiments about a homegrown meter image database containing various meter type images of low contrast, low intensity, shadow, and saturation, it is shown that the proposed numeric character segmentation method performs effectively well for any metering device types under diverse illumination environments.

• Journal of Energy Engineering
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• v.16 no.4
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• pp.202-214
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• 2007

The concept of HSA (Home Service Aggregator) is derived from performing the energy IT business efficiently as well as successfully launcing a new service based on BPL (Broadband over Power Line). The HSA business using a BPL can extend the field of energy industry and an give a chance to create a new demand by consumer-oriented services. This study focuses on the exact evaluation of HSA business using BPL, and reasonable trusty evaluation should be the first step to launch the HSA business. In this study, the categories of cost are comprised of equipment (mainly RSM and MGW) cost, instalation cost, and maintenance cost. AMR (Automatic Meter Reading), internet integration billing service, integration charging service, internet service, sorority service, and electricity safety are listed for benefit. In this study, the ROI of HSA business is 0.9594, which is less than 1. However, that value does not consider the electricity safety benefit which is classified as a social benefit. Therefore, the value can be above 1 if it includes social and private benefits.