• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1387-1390
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• 2005

Recently, foreign utilities emphasize the importance of the value-added services based on Information Technology(IT) as one of the strategic technologies for establishing a new power system in the future digital society. They develop many different types of the value-added services and apply the systems for customer. In domestic case, the data from the Automatic Meter Reading (AMR) System is used only for calculating the tariffs. Data from the AMR system can be strategic assets for utilities to provide the value-added services for customer. Development of the value-added services for utilities and customer needs processing and managing the AMR data. In this paper, the energy consulting service, which is adequate to new power system environment, is suggested for development of the value-added services. The application of the suggested service will bring the effect of reducing the monthly bill for customer. Also the service will give not only the effective demand side management(DSM) and load control, but also reduction of the investment for utilities.

• 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.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term 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.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.245-251
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• 2016

Despite the exponential throughput improvement in mobile communications systems, their ability to satisfy requirements of state-of-the-art and future applications of advanced metering infrastructure (AMI) is still under investigation. Challenges are mainly due to the inadequacy of third generation partnership project (3GPP) networks to support large amounts of devices simultaneously, while the number of AMI end-devices and the frequency of their data transmission increase with new AMI-based applications. In this introductory survey, innovative and future AMI applications and their communication requirements are first reviewed. Then, we identify challenges of 3GPP long term evolution (LTE) in enabling future AMI applications. More importantly, the latest improvements to LTE-A standard release 12 and 13 are reviewed and analyzed with regards to their potential to improve the quality of LTE-enabled AMI. It is found that 3GPP enhancements on machine type communications (MTC) standards will significantly enhance AMI communications. Beyond MTC specifications, non-MTC-specific enhancements such as carrier aggregation and multi-connectivity for user equipment will also contribute greatly to improving reliability and availability of AMI devices. The paper's focus is towards improved backhaul support for innovative and future AMI applications, beyond traditional automatic meter reading.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.245-251
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• 2011

Quantities of data in the DMS (Distribution management system) or SCADA (Supervisory control and data acquisition) system is enormously large as illustrated by the usage of term flooding of data. This enormous quantity of data is transmitted to the status data or event data of the on-site apparatus in real-time. In addition, if GIS (Geographic information system) and AMR (Automatic meter reading), etc are integrated, the quantity of data to be processed in real-time increases unimaginably. Increase in the quantity of data due to addition of system or increase in the on-site facilities cannot be handled through the currently used Single Thread format of data processing technology. However, if Multi Thread technology that utilizes LF-POOL (Leader Follower -POOL) is applied in processing large quantity of data, large quantity of data can be processed in short period of time and the load on the server can be minimized. In this Study, the actual materialization and functions of LF POOL technology are examined.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7B
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• pp.848-857
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• 2011

Due to the advantages of a conveniet, inexpensive installation, flexibility, and scalability, Wireless AMR systems are recently preferred over wired AMR systems. However, a multi-hop supported AMR network, which generally covers large areas, may create energy wastage problem, energy unbalance, and high interference hazard due to a large amount of concurrent-intensive metering data in the network. Therefore in this paper we propose a novel data gathering method which can solve abovementioned problems as well as conserve energy, by reducing the traffic in the network. In addition, the experimental results demonstrate that the proposed scheme shows superior performance to the conventional data transmission method.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.685-688
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• 2008

Planar antennas are very efficient for the use of the out-door data transmission applications due to their low-profile characteristics. But the size of the antenna in UHF band blocks the practical usage. In this paper, a miniaturized planar antenna is investigated for the application of AMR system in UHF band. The designed antenna is fabricated in the form of the microstrip patch on the FR-4 substrate. The miniaturization is achieved by slot-loading on the patch. First, effects of simple slots on the size reduction are analyzed, followed by the design using more complex slots resulting in the reduction ratio of 37.9% in 425.6 MHz and bandwidth of 3.8 MHz. The effect of epoxy coating for the protection is also investigated.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.979-983
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• 2008

Planar antennas are very efficient for the use of the out-door data transmission applications due to their low-profile characteristics. But the size of the antenna in UHF band blocks the practical usage. In this paper, a miniaturized planar antenna is investigated for the application of AMR system in UHF band. The designed antenna is fabricated in the form of the microstrip patch on the FR-4 substrate. The miniaturization is achieved by slot-loading on the patch. First, effects of simple slots on the size reduction are analyzed, followed by the design using more complex slots resulting in the reduction ratio of 37.9% in 425.6 MHz and bandwidth of 3.8 MHz. The effort of epoxy coating for the protection is also investigated.