Electricity load profile classification using Fuzzy C-Means method

Prahastono, Iswan; King, David J.; Ozveren, C.S.; Bradley, David

doi:10.1109/upec.2008.4651527

Cited by 23 publications

(13 citation statements)

References 4 publications

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“…In [40], the experiments include different execution of the FCM in order to calibrate the fuzziness parameters. The results indicate that while the value increases, the clustering error, as measured by 3 validity indicators, decreases.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

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Section: Literature Survey and Contributionsmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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“…From 1970s to 1990s, different types of fuzzy clustering algorithms have been present, one of the most popular algorithms of which is the fuzzy clustering method based on objective function. Fuzzy c-Means algorithm theory (FCM), one of the most effective, was founded by Bezdek in 1974, which has intuitive geometry significance with fast calculating speed and simple processing [10][11].…”

Section: Introductionmentioning

confidence: 99%

Linguistic dynamic analysis for single lane's traffic status based on FCM

Wang

Zhu³

2014

Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined; fuzzy c-means clustering (FCM) algorithm based on fuzzy theory can calculate the clustering center of plenty data quickly and easily; linguistic dynamic systems could describe the dynamic rules of complex systems in the language level. In this paper, membership functions are decided by FCM; result of a specific time period taken as one example is obtained; it's discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method.

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“…TOPSIS was proposed by Hwang and Yoon to solve classical MCDM problems [13]. Applications based on FCM are proposed in several articles [18][19][20][21][22]. One of the most efficient clustering methodologies is fuzzy clustering, and a widely used fuzzy clustering method is the FCM algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization problem for the Thyristor Controlled Series Compensators placement with multiple decision-making approaches

Christa¹,

Venkatesh²

2011

Int. Trans. Electr. Energ. Syst.

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SUMMARY The optimal placement of Thyristor Controlled Series Compensators (TCSCs) in transmission systems is formulated as a multi‐objective optimization problem with the objectives of simultaneously maximizing transmission system loadability, minimizing transmission loss, minimizing the cost of TCSCs, and minimizing the voltage deviation at the load buses. The thermal limits of transmission lines and voltage limits of load buses are considered as security constraints. This problem is solved in two stages. In the first stage, multi‐objective particle swarm optimization, multi‐objective comprehensive learning particle swarm optimizer, and weighted sum method are applied to obtain the non‐dominated solutions. In the second stage, two different multi‐criterion decision‐making tools, namely Technique for Order Preference by Similarity to Ideal Solution and Fuzzy C‐Means clustering are employed to assist the decision maker in deciding the best trade‐off solution. The proposed approach is successfully tested on IEEE 14 bus system and IEEE 118 bus system. Copyright © 2011 John Wiley & Sons, Ltd.

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Electricity load profile classification using Fuzzy C-Means method

Cited by 23 publications

References 4 publications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Linguistic dynamic analysis for single lane's traffic status based on FCM

Multi-objective optimization problem for the Thyristor Controlled Series Compensators placement with multiple decision-making approaches

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