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Warm-Start Meta-Ensembles for Forecasting Energy Consumption in Service Buildings

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

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Abstract

Energy Management Systems are equipments that normally perform the individual supervision of power controllable loads. With the objective of reducing energy costs, those management decisions result from algorithms that select how the different working periods of equipment should be combined, taking into account the usage of the locally generated renewable energy, electricity tariffs etc., while complying with the restrictions imposed by users and electric circuits. Forecasting energy usage, as described in this paper, allows to optimize the management being a major asset.

This paper proposes and compares three new meta-methods for forecasts associated to real-valued time series, applied to the buildings energy consumption case, namely: a meta-method which uses a single regressor (called Sliding Regressor – SR), an ensemble of regressors with no memory of previous fittings (called Bagging Sliding Regressor – BSR), and a warm-start bagging meta-method (called Warm-start Bagging Sliding Regressor – WsBSR). The novelty of this framework is combination of the meta-methods, warm-start ensembles and time series in a forecast framework for energy consumption in buildings. Experimental tests done over data from an hotel show that, the best accuracy is obtained using the second method, though the last one has comparable results with less computational requirements.

This work was supported by the Portuguese Foundation for Science and Technology (FCT), project LARSyS - FCT Project UIDB/50009/2020 and by the European Union, under the FEDER (Fundo Europeu de Desenvolvimento Regional) and INTERREG programs, in the scope of the T2UES (0517_TTUES_6_E) project.

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Author information

Authors and Affiliations

  1. LARSyS, Universidade do Algarve, Faro, Portugal

    Pedro J. S. Cardoso & João M. F. Rodrigues

  2. ISE, Universidade do Algarve, Faro, Portugal

    Pedro J. S. Cardoso, Pedro M. M. Guerreiro, Jânio Monteiro, André S. Pedro & João M. F. Rodrigues

  3. INESC-ID, Lisbon, Portugal

    Jânio Monteiro

Authors
  1. Pedro J. S. Cardoso
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  2. Pedro M. M. Guerreiro
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  3. Jânio Monteiro
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  4. André S. Pedro
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  5. João M. F. Rodrigues
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Corresponding author

Correspondence to Pedro J. S. Cardoso .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Cardoso, P.J.S., Guerreiro, P.M.M., Monteiro, J., Pedro, A.S., Rodrigues, J.M.F. (2021). Warm-Start Meta-Ensembles for Forecasting Energy Consumption in Service Buildings. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_26

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  • DOI: https://doi.org/10.1007/978-3-030-77980-1_26

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