Spatiotemporal Load-Analysis Model for Electric Power Distribution Facilities Using Consumer Meter-Reading Data

Shin, Jin-Ho; Yi, Bong-Jae; Kim, Young Il; Lee, Heon-Gyu; Ryu, Keun Ho

doi:10.1109/tpwrd.2010.2091973

Cited by 20 publications

(21 citation statements)

References 9 publications

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“…To ensure the timeliness of the clustering results, new patterns can be assigned to the obtained clusters by an appropriate classification tool [9], and a re-clustering process will then be conducted once some performance indices such as the classification error (RMSE) is unacceptable. …”

Section: Resultsmentioning

confidence: 99%

“…For comparison purposes, several K-means based methods available in the literature have also been applied to the dataset, including classical K-means [4], [5], [9], [11]- [18], [20], [21], fuzzy K-means [11]- [14], [17], [18], [27]- [29], K-means++ [30], [31], weighted fuzzy average K-means (WFA-K-means) [16], [29], [32], and developed K-means [17]. Moreover, the H-K-means methods with the other numbers of levels (L = 3, 4, 5, 7) are also included.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…Meanwhile, load pattern clustering has also been used to decompose unknown profiles into known profiles, which is desirable for distribution companies to influence the behaviors of the customers [4]. In addition, clustering load patterns with different consumption behaviors is also useful for distribution network state estimation [5], load forecasting [6], demand response [7], market strategies design [8], customer classification [9], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Apart of them, K-means [4], [5], [9], [11]- [18], [20]- [23], is a widely used clustering method with the simplest principle and fast convergence speed. K-means has been applied to the analysis of the large-scale datasets in [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Chiang

Liu

et al. 2017

IEEE Trans. Power Delivery

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Clustering of the load patterns from distribution network customers is of vital importance for several applications. However, the growing number of Advanced Metering Infrastructure (AMI) and a variety of customer behaviors make the clustering task quite challenging due to the increasing amount of load data. K-means is a widely used clustering algorithm in processing large dataset with acceptable computational efficiency, but suffers from local optimal solutions. To address this issue, this paper presents a hierarchical K-means (H-K-means) method for better clustering performance for big data problems. The proposed method is applied to a large-scale AMI dataset and its effectiveness is evaluated by benchmarking with several existing clustering methods in terms of five common adequacy indices, outliers detection, and computation time.Index Terms-Advanced Metering Infrastructure (AMI), big data problems, clustering, hierarchical K-means (H-K-means), load patterns.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Chiang

Liu

et al. 2017

IEEE Trans. Power Delivery

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“…Many predictor techniques have been applied to improve power system performance such as data mining method which uses historical data records . Data mining is used in load forecasting, clustering, and decision tree classification whereas information about load demand ahead of time helps the utilities and electricity suppliers in many ways . Also, data mining methods were used for renewable energy power prediction such as solar power generation .…”

Section: Introductionmentioning

confidence: 99%

Fast quasi-static time-series analysis and reactive power control of unbalanced distribution systems

Selim

Abdel‐Akher

Aly

et al. 2018

Int Trans Electr Energ Syst

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Summary The paper develops a method for solving time‐series power flow for unbalanced distribution power systems equipped with on‐load tap changer and photovoltaic (PV) power generation. The proposed PV control is developed using a fuzzy logic controller. When the reactive power of the PV inverter is not enough, tap‐up or tap‐down signals are sent to the on‐load tap changer to control the voltage. The Lagrange function is implemented to predict the initial values of voltage magnitudes and phase angles during the simulation process. These initial values are corrected using power flow routine that adopts the forward/backward analysis method. The IEEE‐123 node test feeder is used to assess the validity of the developed continuous power flow solution. The results are compared with the traditional quasi‐static time‐series solution without prediction scheme. The simulation results prove that the developed time‐series model has superior performance. The effects of PV penetration and voltage regulator on the method performance and tap setting are also investigated.

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Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end‐consumers. Short‐term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi‐year, and high‐frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid‐sized city. For smart meter aggregates complemented with geo‐specific weather data, we benchmark several state‐of‐the‐art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature‐adjusted auto‐regressive models, exponential smoothing and state‐space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system‐wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. © 2014 Alcatel‐Lucent.

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Spatiotemporal Load-Analysis Model for Electric Power Distribution Facilities Using Consumer Meter-Reading Data

Cited by 20 publications

References 9 publications

Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Fast quasi-static time-series analysis and reactive power control of unbalanced distribution systems

Demand Forecasting in Smart Grids

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