Electricity customer classification using frequencydomain load pattern data
In competitive electricity markets, electricity customer classification is becoming increasingly important, due to new degrees of freedom the electricity providers have been given in formulating dedicated tariff options for different customer classes. Several customer classification techniques have been proposed in the literature, in which the load patterns are typically represented by timedomain data. However, a good load pattern representation requires using several data for each customer, causing possible difficulties in storing a large amount of data in the electricity company's databases. In order to reduce the number of data to be stored for each customer, an original solution is proposed in this paper, based on post-processing the results of timedomain measurements to obtain a reduced set of data defined in the frequency domain. The new set of data is successively used in a customer classification procedure, e.g. a suitable clustering technique, whose adequacy can be assessed by means of properly defined indicators. This paper provides the mathematical background for the frequencydomain data definition and investigates on the effectiveness of the customer classification for different choices of the number of data to be stored. Results obtained on a set of customers belonging to a real distribution system are presented and discussed. These results show that the proposed representation is effective in reducing the number of data stored while maintaining a satisfactory level of classification adequacy.
electricity customer classification load patterns clustering frequencydomain data harmonics-based features