Abstract:
Electric industry is one of most important service provider and back bone of the energy sector in
the world. Ethiopia electric utility is the only national organization distributing electric power in
our country. Electric power industries are being pushed to and quickly respond to the individual
and organization needs and wants of their customers due to the dynamic and highly competitive
nature of the industry. As the researcher noted in the literature, most previous studies developed
Regression and classification models using few data set, two or less data mining algorithms,
dataset also have less quality including inaccurate, inconsistent, incomplete and duplicated
information. Therefore, the prime purpose of the current study was to develop a predictive model
for anticipating the electric power consumption of EEU customers in Arba Minch district. The
study employs and follow data mining techniques and methodologies to attain its objectives. Major
steps followed in the research include: problem understanding, data understanding, data
preparation, modeling, experimentation, and evaluation. The researcher used purposive sampling
method. As a result, due to data availability, researcher exposure, and time constraint the Arba
Minch District electric utility customers’ data covering six months (Sept. 2022 to Feb. 2023) was
considered. The study used four predictive modeling algorithms based on their suitability to build
both regression and classification tasks. Accordingly, the study included: Support Vector Machine,
Decision Tree, Random Forest, and Artificial Neural Network algorithms for classification. The
result achieved from the experiments showed that the Support Vector Machine algorithm
performed best for regression task with root mean squared error (RMSE) of 350.12. Whereas,
Decision Tree algorithm performed best on classification task with accuracy of 62.78%. This study
regresses the power consumption value and also classify power consumption category as either
high or low power consumption considering the nature of the customers. Finally, the study
explored various power related problems and solutions from the analysis of the data and results of
the current study.
