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Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches

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Towards Energy Smart Homes

Abstract

This chapter describes the state-of-the-art methods to forecast energy consumption and energy services in residential buildings. The review spans from model-based approaches—like building thermal simulation tools—to data-driven approaches—like Non-Intrusive Load Monitoring (NILM) or machine learning methods. In particular, the chapter will analyze the context to define which approach should be followed, such as the sampling rate of data and the available data features or even the evolution of equipment and appliances under the new IoT setting. We also discuss the integration of these approaches, like using the model-based technique to generate data from data-driven approaches in context with scarce data or how to use data-driven models to learn model-based models and replace them in the context of real-time applications where the available computational time is low. The chapter provides some examples and benchmarks the different approaches based on literature. In the end, it also discusses the future perspectives for the use and development of these methods.

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Acknowledgements

This paper was developed under the cooperation project PESSOA2019 “INTERACTIVE BEHAVIOUR FOR URBAN FLEXIBILITY” from the Program Hubert Curien (PHC), between the Portuguese National Foundation for Science and Technology (FCT) from Portugal and CAMPUSFRANCE from France.

The contribution from Carlos Santos Silva stems from the research work of multiple doctoral students at IN+ that have contributed to understand the use of energy in the building sector and make it more efficient, namely Henrique Pombeiro, Ricardo Gomes, and Surya Pandiyan.

The contribution from Manar Amayri benefits from the support of the INVOLVED ANR-14-CE22-0020-01 project of the French National Research Agency. It is also supported by the French National Research Agency in the framework of the EquipEx program AmiQual4Home ANR -11-EQPX-00 and the Investissements d’avenir program (ANR-15-IDEX-02) as the cross disciplinary program Eco-SESA. It is also supported by the project Pessoa2019.

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Authors and Affiliations

  1. IN+ Centre for Innovation, Technology and Policy Research, LARSYS, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Carlos A. Santos Silva

  2. Grenoble, CNRS, France

    Manar Amayri

  3. Quby B.V., Amsterdam, Netherlands

    Kaustav Basu

Authors
  1. Carlos A. Santos Silva
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  2. Manar Amayri
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  3. Kaustav Basu
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Correspondence to Carlos A. Santos Silva .

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Editors and Affiliations

  1. G-SCOP lab, Grenoble Institute of Technology University of Grenoble Alps, Grenoble, France

    Stephane Ploix

  2. CNRS, Grenoble, France

    Manar Amayri

  3. CIISE, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

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Silva, C.A.S., Amayri, M., Basu, K. (2021). Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches. In: Ploix, S., Amayri, M., Bouguila, N. (eds) Towards Energy Smart Homes. Springer, Cham. https://doi.org/10.1007/978-3-030-76477-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-76477-7_7

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