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

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 grou...

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Bibliographic Details
Published in:Energies (Basel) Vol. 10; no. 9; p. 1407
Main Authors: Panapakidis, Ioannis, Asimopoulos, Nikolaos, Dagoumas, Athanasios, Christoforidis, Georgios C.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2017
Subjects:
Algorithms
Clustering
demand response
Demand side management
load management
load modeling
load profiles
optimization
time-series clustering
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en10091407