• Title/Summary/Keyword: Customer Load Pattern

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A Study for CBL(Customer Baseline Load) utilization in Day Ahead Demand Response operation (상시수요응답(Day Ahead Demand Response) 운영에서의 CBL 활용방안 연구)

  • Ko, Jong-Min;Yang, Il-Kwon;Song, Jae-Ju;Jin, Sung-Il
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.1
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    • pp.28-34
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    • 2009
  • In this study firstly we survey the calculation method and the characteristics of the way of estimating CBL(Customer BaseLine Load) that is important calculation tool for DRP internationally. Also we analyze the power consumption pattern using the 15 minutes load profiles of about 120,000 customers in domestic. Based on this pattern, we provide the CBL calculation method that can be utilized in DRP to save the cost, and analyze the accuracy of the CBL calculation proposed in this paper through the simulation.

Overload Criteria of Distribution Transformers Considering the Electric Consumption Patterns of Customers (수용가 전력 소비 패턴을 고려한 배전용 변압기 과부하 판정기준)

  • 윤상윤;김재철
    • 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.

Customer Load Pattern Analysis using Clustering Techniques (클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석)

  • Ryu, Seunghyoung;Kim, Hongseok;Oh, Doeun;No, Jaekoo
    • KEPCO Journal on Electric Power and Energy
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    • v.2 no.1
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    • pp.61-69
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    • 2016
  • Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

CLUSTER ANALYSIS FOR REGION ELECTRIC LOAD FORECASTING SYSTEM

  • Park, Hong-Kyu;Kim, Young-Il;Park, Jin-Hyoung;Ryu, Keun-Ho
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.591-593
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    • 2007
  • This paper is to cluster the AMR (Automatic Meter Reading) data. The load survey system has been applied to record the power consumption of sampling the contract assortment in KEPRI AMR. The effect of the contract assortment change to the customer power consumption is determined by executing the clustering on the load survey results. We can supply the power to customer according to usage to the analysis cluster. The Korea a class of the electricity supply type is less than other country. Because of the Korea electricity markets exists one electricity provider. Need to further divide of electricity supply type for more efficient supply. We are found pattern that is different from supplied type to customer. Out experiment use the Clementine which data mining tools.

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Development of Representative Curves for Classified Demand Patterns of the Electricity Customer

  • Yu, In-Hyeob;Lee, Jin-Ki;Ko, Jong-Min;Kim, Sun-Ic
    • 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.

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A Study on Demand Pattern Analysis for Forecasting of Customer's Electricity Demand (수요측 전력사용량 예측을 위한 수요패턴 분석 연구)

  • Ko, Jong-Min;Yang, Il-Kwon;Yu, In-Hyeob
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.8
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    • pp.1342-1348
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    • 2008
  • One important objective of the electricity market is to decrease the price by ensuring stability in the market operation. Interconnected to this is another objective; namely, to realize sustainable consumption of electricity by equitably distributing the effects and benefits of participating in the market among all participants of the industry. One method that can help achieve these objectives is the ^{(R)}$demand-response program, - which allows for active adjustment of the loadage from the demand side in response to the price. The demand-response program requires a customer baseline load (CBL), a criterion of calculating the success of decreases in demand. This study was conducted in order to calculate undistorted CBL by analyzing the correlations between such external or seasonal factors as temperature, humidity, and discomfort indices and the amounts of electricity consumed. The method and findings of this study are accordingly explicated.

Customer Characteristics Modeling for Each Load Pattern using the Database (데이터베이스를 이용한 부하패턴별 수용가 특징 모델링)

  • Lee, Young-Suk;Kim, Jae-Chul;Oh, Jung-Hwan;Yun, Sang-Yun;Park, Chang-Ho
    • Proceedings of the KIEE Conference
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    • 2001.07a
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    • pp.416-418
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    • 2001
  • This Paper presents the 2-step load cycle of daily load curve for representative load pattern of power distribution transformer. We decide the representative load pattern of distribution transformer in domestic using the pattern classification algorithm. The K-mean method is used for the pattern classification algorithm. The acquisition equipment of field load data is utilized for 96-sample distribution transformers and the field data is used in the construction of the database for the creation of daily load pattern.

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Pattern Classification of Load Demand for Distribution Transformer (배전용 변압기 부하사용 패턴분류)

  • Yun, Sang-Yun;Kim, Jae-Chul;Lee, Young-Suk
    • Proceedings of the KIEE Conference
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    • 2001.05a
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    • pp.89-91
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    • 2001
  • This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.

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2-Step Modeling for Daily Load Curve of Up to and Including 100kVA Distribution Transformer (100kVA 이하급 배전용 변압기 일부하 패턴의 2-Step 모델링)

  • Lee, Young-Suk;Kim, Jae-Chul;Yun, Sang-Yun
    • Proceedings of the KIEE Conference
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    • 2001.11b
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    • pp.371-373
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    • 2001
  • In this paper, we present 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. Daily load patterns are classified by two methods dependent upon possession information. In case we possess daily load profiles make use of K-mean algorithm and in case we have not daily load profiles, make use of customer information of KEPCO. As the parameters of the load pattern classification, we use are daily load profiles and customer information of each distribution transformers. Data management system is used for NT oracle. We can present peak load magnitude, initial load magnitude and peak load duration for daily load patterns by 2-step load cycle for daily load curve of up to and including 100kVA distribution transformer in domestic. We think that this paper contributes to enhancing the distribution transformer overload criterion.

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Classification Methods for Automated Prediction of Power Load Patterns (전력 부하 패턴 자동 예측을 위한 분류 기법)

  • Minghao, Piao;Park, Jin-Hyung;Lee, Heon-Gyu;Ryu, Keun-Ho
    • 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.

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