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Energies 2017, 10(9), 1407; doi:10.3390/en10091407

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

1
Department of Electrical Engineering, Western Macedonia University of Applied Sciences, Kozani 50100, Greece
2
Department of Electrical Engineering, Technological Educational Institute of Thessaly, Larisa 41110, Greece
3
Energy and Environmental Policy Laboratory, School of Economics, Business and International Studies, University of Piraeus, Piraeus 18532, Greece
*
Authors to whom correspondence should be addressed.
Received: 6 August 2017 / Revised: 4 September 2017 / Accepted: 12 September 2017 / Published: 14 September 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [4350 KB, uploaded 21 September 2017]   |  

Abstract

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets. View Full-Text
Keywords: demand response; load management; load modeling; load profiles; optimization; time-series clustering demand response; load management; load modeling; load profiles; optimization; time-series clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Panapakidis, I.; Asimopoulos, N.; Dagoumas, A.; Christoforidis, G.C. An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures. Energies 2017, 10, 1407.

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