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2024-12-17
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info:eu-repo/semantics/openAccess
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Tech Science Press
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Resumen
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data. The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population. For the first time, a step function approach is applied to reduce time series dimensionality. Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage. In addition, a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes: available Energy (E), Temporal patterns (T), Consistency (C), and Variability (V). The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise (SME) customers, identifying 4 daily and 6 hourly easy-to-interpret, well-defined clusters. The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results. Further research can test the scalability of the methodology to larger datasets from various customer segments (households and large commercial) and locations with different weather and socioeconomic conditions.
Descripción
The registered version of this article, first published in Computer Modeling in Engineering & Sciences, 142(1), 869–907, is available online at the publisher's website: https://doi.org/10.32604/cmes.2024.054946
La versión registrada de este artículo, publicado por primera vez en Computer Modeling in Engineering & Sciences, 142(1), 869–907, está disponible en línea en el sitio web del editor: https://doi.org/10.32604/cmes.2024.054946
This work was partly supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00. This publication is also supported by Iberdrola S. A. as part of its innovation department research studies. Its contents are solely the authors’ responsibility and do not necessarily represent the official views of Iberdrola Group. The primary author thanks Iberdrola for its support in providing a virtual machine to undertake computer simulations.
La versión registrada de este artículo, publicado por primera vez en Computer Modeling in Engineering & Sciences, 142(1), 869–907, está disponible en línea en el sitio web del editor: https://doi.org/10.32604/cmes.2024.054946
This work was partly supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00. This publication is also supported by Iberdrola S. A. as part of its innovation department research studies. Its contents are solely the authors’ responsibility and do not necessarily represent the official views of Iberdrola Group. The primary author thanks Iberdrola for its support in providing a virtual machine to undertake computer simulations.
Categorías UNESCO
Palabras clave
Electric load clustering, load profiling, smart meters, machine learning, data mining, demand flexibility, demand response
Citación
Bañales, S., Dormido, R., Duro, N. (2025). Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers. Computer Modeling in Engineering & Sciences, 142(1), 869–907. https://doi.org/10.32604/cmes.2024.054946
Centro
E.T.S. de Ingeniería Informática
Departamento
Informática y Automática