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October 27, 2025Data3 citationsOpen Access

Electrical Measurement Dataset from a University Laboratory for Smart Energy Applications

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SSSergio D. Saldarriaga-ZuluagaJVJosé Ricardo Velasco-MéndezCMCarlos Mario Moreno-Paniagua

Key Points

  • The curated dataset captures detailed electrical behavior, enhancing understanding of energy consumption and power quality.
  • Comprising approximately 34,128 entries, data includes three-phase voltages, currents, and power metrics recorded every 10 minutes.
  • Collected using high-accuracy power analyzers, the dataset supports machine learning models for smart energy applications.
  • Open access to this dataset may enable advancements in demand forecasting and energy efficiency studies.

Abstract

Continuous monitoring of electrical parameters is essential for understanding energy consumption, assessing power quality, and analyzing load behavior. This paper presents a dataset comprising measurements of three-phase voltages and currents, active and reactive power (per phase and total), power factor, and system frequency. The data was collected between April and December 2024 in the low-voltage system of a university laboratory, using high-accuracy power analyzers installed at the point of common coupling. Measurements were recorded every 10 min, generating 79 files with 432 records each, for a total of approximately 34,128 entries. To ensure data quality, the values were validated, erroneous entries removed, and consistency verified using power triangle relationships. The curated dataset is provided in tabular (CSV) format, with each record including a timestamp, three-phase voltages, three-phase currents, active and reactive power (per phase and total), power factor (per phase and global), and system frequency. This dataset offers a comprehensive characterization of electrical behavior in a university laboratory over a nine-month period. It is openly available for reuse and can support research in power system analysis, renewable energy integration, demand forecasting, energy efficiency, and the development of machine learning models for smart energy applications.

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Cite This Study

Saldarriaga-Zuluaga et al. (2025) studied this question.

synapsesocial.com/papers/68ff87f1c8c50a61f2bdd6f1https://doi.org/10.3390/data10110170
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