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A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings

Author

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  • Roberto Chiosa

    (Department of Energy “Galileo Ferraris”, TEBE Research Group, BAEDA Lab, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Marco Savino Piscitelli

    (Department of Energy “Galileo Ferraris”, TEBE Research Group, BAEDA Lab, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Alfonso Capozzoli

    (Department of Energy “Galileo Ferraris”, TEBE Research Group, BAEDA Lab, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

Abstract

Recently, the spread of smart metering infrastructures has enabled the easier collection of building-related data. It has been proven that a proper analysis of such data can bring significant benefits for the characterization of building performance and spotting valuable saving opportunities. More and more researchers worldwide are focused on the development of more robust frameworks of analysis capable of extracting from meter-level data useful information to enhance the process of energy management in buildings, for instance, by detecting inefficiencies or anomalous energy behavior during operation. This paper proposes an innovative anomaly detection and diagnosis (ADD) methodology to automatically detect at whole-building meter level anomalous energy consumption and then perform a diagnosis on the sub-loads responsible for anomalous patterns. The process consists of multiple steps combining data analytics techniques. A set of evolutionary classification trees is developed to discover frequent and infrequent aggregated energy patterns, properly transformed through an adaptive symbolic aggregate approximation (aSAX) process. Then a post-mining analysis based on association rule mining (ARM) is performed to discover the main sub-loads which mostly affect the anomaly detected at the whole-building level. The methodology is developed and tested on monitored data of a medium voltage/low voltage (MV/LV) transformation cabin of a university campus.

Suggested Citation

  • Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:1:p:237-:d:474846
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    References listed on IDEAS

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    5. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.
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    7. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

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