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In Finland, customer class load profiles are used extensively in distribution network calculation. State estimation systems, for example, use the load profiles to estimate the state of the network. Load profiles are also needed to predict future loads in distribution network planning. In general, customer class load profiles are obtained through sampling in load research projects. Currently, in Finland, customer classification is based on the uncertain customer information found in the customer information system. Customer information, such as customer type, heating solution, and tariff, is used to connect the customers with corresponding customer class load profiles. Now that the automatic meter-reading systems are becoming more common, customer classification and load profiling could be done according to actual consumption data. This paper proposes the use of the ISODATA algorithm for customer classification. The proposed customer classification and load profiling method also includes temperature dependency correction and outlier filtering. The method is demonstrated in this paper by studying a set of 660 hourly metered customers.
Mutanen et al. (Fri,) studied this question.