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Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms

Author

Listed:
  • Shuo-Yan Chou

    (Taiwan Building Technology Center, National Taiwan University of Science and Technology, Taipei 106, Taiwan
    Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Anindhita Dewabharata

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Ferani E. Zulvia

    (Department of Logistics Engineering, Universitas Pertamina, Jakarta 12220, Indonesia)

  • Mochamad Fadil

    (Department of Logistics Engineering, Universitas Pertamina, Jakarta 12220, Indonesia)

Abstract

A building, a central location of human activities, is equipped with many devices that consume a lot of electricity. Therefore, predicting the energy consumption of a building is essential because it helps the building management to make better energy management policies. Thus, predicting energy consumption of a building is very important, and this study proposes a forecasting framework for energy consumption of a building. The proposed framework combines a decomposition method with a forecasting algorithm. This study applies two decomposition algorithms, namely the empirical mode decomposition and wavelet transformation. Furthermore, it applies the long short term memory algorithm to predict energy consumption. This study applies the proposed framework to predict the energy consumption of 20 buildings. The buildings are located in different time zones and have different functionalities. The experiment results reveal that the best forecasting algorithm applies the long short term memory algorithm with the empirical mode decomposition. In addition to the proposed framework, this research also provides the recommendation of the forecasting model for each building. The result of this study could enrich the study about the building energy forecasting approach. The proposed framework also can be applied to the real case of electricity consumption.

Suggested Citation

  • Shuo-Yan Chou & Anindhita Dewabharata & Ferani E. Zulvia & Mochamad Fadil, 2022. "Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms," Energies, MDPI, vol. 15(3), pages 1-35, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1035-:d:738573
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    References listed on IDEAS

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    1. Kneifel, Joshua & Webb, David, 2016. "Predicting energy performance of a net-zero energy building: A statistical approach," Applied Energy, Elsevier, vol. 178(C), pages 468-483.
    2. Saidur, R. & Masjuki, H.H. & Jamaluddin, M.Y., 2007. "An application of energy and exergy analysis in residential sector of Malaysia," Energy Policy, Elsevier, vol. 35(2), pages 1050-1063, February.
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    Cited by:

    1. Yongrui Qin & Meng Zhao & Qingcheng Lin & Xuefeng Li & Jing Ji, 2022. "Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN," Mathematics, MDPI, vol. 10(17), pages 1-10, August.
    2. Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.

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