In a deregulated energy environment, consumers can purchase electricity from any provider ... Many techniques for consumers' classification have been.
ELECTRICITY CONSUMER CLASSIFICATION USING ARTIFICIAL INTELLIGENCE
K.L. Lo, Zuhaina Zakaria University of Strathclyde, United Kingdom
ABSTRACT In a deregulated energy environment, consumers can purchase electricity from any provider regardless of size and location. As a result, there is a growing interest in understanding the nature of variations in consumer’s consumption. This information can be used to facilitate electricity supplier in their marketing strategy. Thus, it is essential to have typical load profiles of different groups of consumers. Many techniques for consumers’ classification have been reported in the past. The techniques include applications of statistics, unsupervised clustering technique and methods based on frequency domain approach. This paper examined the capability of artificial intelligent techniques to classify electricity consumers by their pattern of consumption. Fuzzy clustering and artificial neural network (ANN) has been employed in this study. Results obtained demonstrate the ability of the proposed method in classifjmg consumer by their energy consumption,
Keywords: fizzy c-means, load profiling, classification
INTRODUCTION such a system. Therefore, there is a need to search for alternative tools that would provide satisfactory and cost-effective approach.
For many years, the electricity power industries have been dominated by large utilities that had an ovaall authority over all activities in generation, transmission and distribution of power within its domain of operation. However, in 1990s electricity market liberalization was introduced. In this environment, distribution companies have more freedom in the determining the tariff rates abide by a set of regulatory rules. On the other hand, consumers also have the flexibility to choose their electricity provider. Therefore, distribution companies must equip themselves with a suitable tariff formulation and better marketing strategies. Detailed knowledge on consumer’s load consumption can facilitate distribution companies in determining specific tariff options for different type of consumers.
Efforts toward this aim have already been made and load profiling has emerged as one of suitable methods. Profiling enables an electricity supplier to calculate the electricity consumption for every pricing period on the market of its customers who do not have a tariff meter. This will form the basis for the supplier to pay for the electricity purchased &om the wholesale market. There are two general load profile models i.e. the area and the category model. The category model grouped customers with a similar load pattern into categories. Each individual customer is then associated with a predetermined representative load profile. The category model is rather a popular practice and is the focus of this paper. However, there is always the precondition that sufficient load measurement have been made earlier.
Ideally, the most efficient method to determine consumer’s electricity consumption would be the direct monitoring of their daily load diagrams. This can be achieved by installing time interval meters, quarterhourly, half-hourly or hourly at each point of consumption. However, this approach is costprohibitive due to the equipment and processing costs. For example, in United Kingdom, half-hour tariff meters are installed for customers with a maximum demand of 100 KW and above, at their premise. Therefore, it is easy to establish at which price the electricity consumption should be billed. However, from June 1999 about 26 million customers, majority in residential sector also have access to the open market. Providing half-hourly meter for every customer below 100 KW is not considered practical. Furthermore, a significant amount of time would be needed to develop
This paper examined the capability of artificial intelligent techniques to cluster electricity consumers based on their consumption pattern. Initiaily, consumers may be grouped according their type of activity, such as residential and non-residential [I, 21, However, [3, 41 reported that there are poor correlation between type of activity and electricity consumption of the consumer. Therefore, more stubes need to be carried out in developing a good customer classification.
According to [SI the major classification techniques in electricity customers’ are feature selection, time domain approaches (unsupervised clustering algorithm, statistics
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application, neural networks, fuzzy system) and frequency domain approaches (harmonic analysis, wavelets). Researches in these methods were reported in several papers [6-111. Although they used different form of classification techniques but their main objective i s to obtain groupings of similar load profiles.
where N = number of load profile C = number of cluster m = weighting parameter, in general m=2 uy = is the degree of membership of 1,in the cluster j xi = is the profile of ith feeder of measured data, c, =jth cluster centre 11*11 = is any norm expressing the similarity between any measured data and the centre
Fuzzy c-means (FCM) and artificial neural network (ANN) clustering has been employed in this study. The Ioad data used in this work are daily load curves of different consumers derived from a distribution network. Results obtained demonstrate the ability of the proposed method in classifying consumer by their energy consumption.
Consider a set o f N load profiles X={XI,X~, ...,x ~ } t obe clustered into C clusters (l
