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

Author

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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
    These authors contributed equally to this work.)

  • Manuel Ayala-Chauvin

    (Centro de Investigación en Ciencias Humanas y de la Educación (CICHE), Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato 180103, Ecuador
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Darío Muyulema-Masaquiza & Manuel Ayala-Chauvin, 2025. "Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems," Energies, MDPI, vol. 18(12), pages 1-30, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3083-:d:1676641
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    References listed on IDEAS

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    1. Lucas Henriques & Felipe Prata Lima & Cecilia Castro, 2024. "Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns," Future Internet, MDPI, vol. 16(7), pages 1-23, June.
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    3. Michalakopoulos, Vasilis & Sarmas, Elissaios & Papias, Ioannis & Skaloumpakas, Panagiotis & Marinakis, Vangelis & Doukas, Haris, 2024. "A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs," Applied Energy, Elsevier, vol. 361(C).
    4. Maher AbuBaker, 2019. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine," Energies, MDPI, vol. 12(22), pages 1-29, November.
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