Evaluation of the performance of clustering algorithms for a high voltage industrial consumer. (February 2015)

• Record Type: Journal Article
• Authors: Panapakidis, Ioannis; Alexiadis, Minas; Papagiannis, Grigoris
• Is Part Of: Engineering applications of artificial intelligence. Volume 38(2015:Feb.)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-02
• Subjects: Demand side management -- Load modeling -- Load profiles -- Conditional entropy minimization clustering -- Unsupervised machine learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2014.10.013
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Load profiling refers to a procedure which leads to the formulation of daily load curve clusters based on the similarity of the curves shapes. This paper focuses on the investigation of the consumption patterns of an existing high voltage industrial consumer. The profiling process involves stages like the normalization of the recorded load data, the utilization of pattern recognition algorithms, the selection of the appropriate validation scheme and the exploitation of the profiling findings. Certain improvements are proposed for each of these stages. More specifically, the most common algorithms of the related literature are implemented and a detailed investigation of their performance is presented. A new algorithm is proposed, presenting, in the majority of the cases, the best performance. Additionally, all the clustering validity indicators of the literature are considered to evaluate the clustering results. After the formulation of the load curve clusters, the load profiles are extracted and based on specific indices conclusions are drawn regarding the implementation of suitable demand side management schemes.