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

Abstract

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.

A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

“…In order to classify the group households precisely, the households in the community were clustered according to the peak consumption times and timetables of residents, such as the morning, noon, evening peak time, wake up time, and bedtime. In this paper, the adopted clustering method is fuzzy c-means clustering (FCM) [28][29][30]. Using the FCM algorithm, group households can be classified into different categories based on the consumption time information for each family.…”

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

“…It should be noted that apart from extracting information about demand patterns, load profiling is an important tool that has been employed in various applications such as load forecasting, retailer profit maximization, scenarios generation for optimization problems, demand side management implementation, load dispatching and others [78,[83][84][85][86][87][88]. The combination of clustering and forecasting system is a promising approach [84].…”

“…As in the case of the K-means, FCM starts by the random selection of the initial centroids. The Improved FCM (IFCM) is introduced in [78] to address the aforementioned problem. The IFCM includes the execution of the K-means in its starting phase in order to cluster the set Y in k clusters and hence, the initial c k centroids are obtained.…”

“…The initial centroids are selected in a random manner. To overcome this limitation, an improved version of the FCM is proposed in [78] as a part of a demand side management methodology to manage the consumption of high voltage industrial consumers. The algorithms are compared with 4 validity indicators and in all cases, the improved version results in lower errors.…”

“…The IFCM is a hybrid algorithm that includes a clustering algorithm to extract the initial matrix U. According to [78], any clustering algorithm can be used for matrix initialization and thus, the input parameter requirements can be reduced if another algorithm is used. Hopfield ANN requires only the maximum number of iterations, a fact that makes it the most suitable neural-network based algorithm according to Criterion#1.…”