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Shapelets to Classify Energy Demand Time Series

Author

Listed:
  • Marco G. Pinheiro

    (Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    EDP, Av. 24 de Julho 12, 1249-300 Lisbon, Portugal)

  • Sara C. Madeira

    (LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal)

  • Alexandre P. Francisco

    (Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    INESC-ID Lisboa, Rua Alves Redol 9, 1000-029 Lisbon, Portugal)

Abstract

Data are an important asset that the electric power industry have available today to support management decisions, excel in operational efficiency, and be more competitive. The advent of smart grids has increased power grid sensorization and so, too, the data availability. However, the inability to recognize the value of data beyond the siloed application in which data are collected is seen as a barrier. Power load time series are one of the most important types of data collected by utilities, because of the inherent information in them (e.g., power load time series comprehend human behavior, economic momentum, and other trends). The area of time series analysis in the energy domain is attracting considerable interest because of growing available data as more sensorization is deployed in power grids. This study considers the shapelet technique to create interpretable classifiers for four use cases. The study systematically applied the shapelet technique to data from different hierarchical power levels (national, primary power substations, and secondary power substations). The study has experimentally shown shapelets as a technique that embraces the interpretability and accuracy of the learning models, the ability to extract interpretable patterns and knowledge, and the ability to recognize and monetize the value of the data, important subjects to reinforce the importance of data-driven services within the energy sector.

Suggested Citation

  • Marco G. Pinheiro & Sara C. Madeira & Alexandre P. Francisco, 2022. "Shapelets to Classify Energy Demand Time Series," Energies, MDPI, vol. 15(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2960-:d:796355
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    References listed on IDEAS

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    1. Schill, Wolf-Peter, 2020. "Electricity Storage and the Renewable Energy Transition," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 4(10), pages 2059-2064.
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    3. Kyriaki Psara & Christina Papadimitriou & Marily Efstratiadi & Sotiris Tsakanikas & Panos Papadopoulos & Paul Tobin, 2022. "European Energy Regulatory, Socioeconomic, and Organizational Aspects: An Analysis of Barriers Related to Data-Driven Services across Electricity Sectors," Energies, MDPI, vol. 15(6), pages 1-25, March.
    4. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
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    Cited by:

    1. Ulvi Rzazade & Sergey Deryabin & Igor Temkin & Egor Kondratev & Alexander Ivannikov, 2023. "On the Issue of the Creation and Functioning of Energy Efficiency Management Systems for Technological Processes of Mining Enterprises," Energies, MDPI, vol. 16(13), pages 1-21, June.
    2. Cezar-Petre Simion & Cătălin-Alexandru Verdeș & Alexandra-Andreea Mironescu & Florin-Gabriel Anghel, 2023. "Digitalization in Energy Production, Distribution, and Consumption: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-30, February.

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