Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

Jimyung Kang,Jee-Hyong Lee

doi:10.3390/en81012242

Abstract

The clustering of electricity customers might have an effective meaning if, and only if, it is verified by domain experts. Most of the previous studies on customer clustering, however, do not consider real applications, but only the structure of clusters. Therefore, there is no guarantee that the clustering results are applicable to real domains. In other words, the results might not coincide with those of domain experts. In this paper, we focus on formulating clusters that are applicable to real applications based on domain expert knowledge. More specifically, we try to define a distance between customers that generates clusters that are applicable to demand response applications. First, the k-sliding distance, which is a new distance between two electricity customers, is proposed for customer clustering. The effect of k-sliding distance is verified by expert knowledge. Second, a genetic programming framework is proposed to automatically determine a more improved distance measure. The distance measure generated by our framework can be considered as a reflection of the clustering principles of domain experts. The results of the genetic programming demonstrate the possibility of deriving clustering principles.

Highlights

A smart grid, which is the combination of an electricity network and information network, provides many new applications in traditional electricity generation, distribution, and consumption areas
We propose a genetic programming (GP) framework that automatically identifies the clustering principle of the experts
The first scenario, which is the main scenario in the experiments, is the clustering of customers based on Representative Load Profile (RLP)

Summary

Introduction

A smart grid, which is the combination of an electricity network and information network, provides many new applications in traditional electricity generation, distribution, and consumption areas. We propose a good distance measure between customers that fits the customer clustering, especially in demand response applications, based on expert knowledge. This new distance measure, which considers the time-shift characteristic of customers, will be explained and verified . The k-sliding distance, which is a new distance measure, is proposed and validated for electricity customer clustering This distance is designed based on the time-shift characteristic of loads and is designed to produce an improved clustering result, especially in demand response applications. The proposal of this new distance is based on the time-series pattern of electricity consumption data. The proposed distance measure can be applicable to other features and can be applied to other clustering algorithms

Experimental Setup

Experimental Result

Finding Clustering Principle Using Genetic Programming

Assessment of Distance According to Genetic Programming

Findings

Conclusions