LOAD PROFILING APPROACH IN CLUSTERING AUTOMATED ELECTRICAL POWER METERING SYSTEM

In Nigeria, customers’ complaints and lean revenue generation are the major challenges in Electricity market. Customers’ complaints range from over billing, estimate billing to under billing. Cases of lean revenue collection with respect to energy supplied are mostly due to unstructured consumer classification. This study ushers in an established and alternative means of electricity customer classification using unsupervised classification technique. The study provides an opportunity using load profiling and clustering technique to build representative electricity consumer load profile as new electricity customer classification depicting levels of customer types which includes private customers and their representative profiles. Load data which are monthly consumers’ consumption load value (in kilowatt-hour) were mined and clustered using a combination of k-means R algorithm and python programming language, while the result was presented in k-means R. The three clusters formed by the k-means R algorithm describe uniquely main category of customer types. Other sub clusters describing different private customer groups were also realized using k-means R algorithm by breaking up further residential customer category. High qualities of cluster groups representing distinctive load profile class were tested for convergence and stability by evaluating total percentage within sum of square error for levels of iterations. This technique was tested by clustering consumers of Enugu Electricity Distribution Company (EEDC) particularly within the state metropolis (New Heaven District) as a new electricity customer classification to generate customers’ electricity tariff to change reliance on old tariff classification generated by questionnaires. The K-means R result showed four private consumer profiles. It proved the identification of consumers to different profiles including consumers within suburbs. It is on the premises that clustering technique to load profiling is deemed so important for study and recommended to Distribution companies so as to strengthen tariff and enhance revenue collection by predicting right monthly consumption based on k-means technique.