Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems

Darío Muyulema-Masaquiza and Manuel Ayala-Chauvin ()
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Darío Muyulema-Masaquiza: Centro de Investigación en Mecatrónica y Sistemas Interactivos (MIST), Facultad de Ingenierías, Maestría en Big Data y Ciencia de Datos, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador
Manuel Ayala-Chauvin: Centro de Investigación en Ciencias Humanas y de la Educación (CICHE), Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador

Energies, 2025, vol. 18, issue 12, 1-30

Abstract: The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R 2 of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure.

Keywords: energy segmentation; K-Means; demand forecasting; anomaly detection; electricity pricing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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