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Design of Ensemble Forecasting Models for Home Energy Management Systems

Author

Listed:
  • Karol Bot

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal)

  • Samira Santos

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal)

  • Inoussa Laouali

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    SIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez 1049-001, Morocco)

  • Antonio Ruano

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1950-044 Lisboa, Portugal)

  • Maria da Graça Ruano

    (Faculty of Science & Technology, University of Algarve, 8005-294 Faro, Portugal
    CISUC, University of Coimbra, 3030-290 Coimbra, Portugal)

Abstract

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

Suggested Citation

  • Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7664-:d:680311
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    Cited by:

    1. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    2. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    3. Ismail Aouichak & Sébastien Jacques & Sébastien Bissey & Cédric Reymond & Téo Besson & Jean-Charles Le Bunetel, 2022. "A Bidirectional Grid-Connected DC–AC Converter for Autonomous and Intelligent Electricity Storage in the Residential Sector," Energies, MDPI, vol. 15(3), pages 1-19, February.
    4. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.

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