Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters

Abstract

Abstract--This paper shows the capacity of modern computational techniques such as the self-organizing map (SOM) as a methodology to achieve the classification of the electrical customers in a commercial or geographical area. This approach allows to extract the pattern of customer behavior from historic load demand series. Several ways of data analysis from load curves can be used to get different input data to "feed" the neural network. In this work, we propose two methods to improve customer clustering: the use of frequency-based indices and the use of the hourly load curve. Results of a case study developed on a set of different spanish customers and a comparison between the two approachs proposed here are presented.

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

“…Unsupervised learning based on SOMs have been used in [25][26][27] to classify, filter and identify customers' consumption patterns in order to learn both their distribution and topology, and segment the demand patterns for electrical customers. Additionally frequencybased indices and hourly LP curve methods were proposed in [28]. In [29][30], authors have modified the Follow-The-Leader algorithm and proposed a frequencydomain approach with SOMs for consumption patternbased classification of electricity consumers in order to know accurate knowledge of the customers' consumption patterns.…”

Description of Two-Peak Characteristics in Power Engineering

“…classification of consumers by their types [20,21]; calculation of losses in the transmission line [22]; studying of the consumer behaviour [23]. Description approaches are most developed for short-term forecasting of daily graphs.…”

Behavioral Profiles for Building Energy Performance Using eXclusive SOM

“…[12]) or the identification of energy user profiles (e.g. [20]). A well-known problem in clustering is the existence of outlying elements that disturb the reliability of the results.…”