THE DATA MINING APPROACH FOR DETERMINING POWER CONSUMPTION OF NIGERIAN ELECTRIC UTILITY CUSTOMERS
The electric industry plays a crucial role as a major service provider and the backbone of the energy sector worldwide. In Nigeria, the sole national organization responsible for distributing electric power is the Nigeria Electric Utility. Due to the dynamic and highly competitive nature of the industry, electric power companies are under pressure to rapidly respond to the diverse needs and demands of both individual and organizational customers. According to a report published by Energy Pedia in 2016, only 27% of the Nigerian population has access to the electricity grid. To address this issue and better understand power consumption patterns, this study aims to design a predictive model using data mining techniques for Nigerian electric utility customers. The research employs a hybrid data mining methodology, which involves classifying customers based on their power consumption and developing a prediction model using classification algorithms. The study progresses through various steps, including problem understanding, data understanding, data preparation, modeling, knowledge discovery evaluation, and the design of a user interface to utilize the discovered knowledge. The dataset used for mining spans from January 2008 to January 2011 E.C. and includes information from all Nigeria utility customers, containing 14 attributes with 85,849 instances. Four classification algorithms—J48, bagging, random tree, and PART—were employed to build the predictive model. Among these, the J48 algorithm achieved the highest accuracy of 96.61%. This model correctly classified 82,939 instances (96.61%) and misclassified 2,910 instances (3.38%). The study focuses on predicting power consumption for new connections of electric utility customers, differentiating between high and low power consumption. Based on the findings, the researcher recommends further study and the development of a system to optimize power consumption management for the electric industry.
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