IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1035-d738573.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1035/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1035/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Filippín, Celina & Ricard, Florencia & Flores Larsen, Silvana & Santamouris, Mattheos, 2017. "Retrospective analysis of the energy consumption of single-family dwellings in central Argentina. Retrofitting and adaptation to the climate change," Renewable Energy, Elsevier, vol. 101(C), pages 1226-1241.
    3. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    4. Christopher J. Koroneos & Evanthia A. Nanaki & George A. Xydis, 2012. "Sustainability Indicators for the Use of Resources—The Exergy Approach," Sustainability, MDPI, vol. 4(8), pages 1-12, August.
    5. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre for Inclusive Growth.
    6. Mohammed Awad Abuhussain & Nedhal Al-Tamimi & Badr S. Alotaibi & Manoj Kumar Singh & Sanjay Kumar & Rana Elnaklah, 2022. "Impact of Courtyard Concept on Energy Efficiency and Home Privacy in Saudi Arabia," Energies, MDPI, vol. 15(15), pages 1-18, August.
    7. BoroumandJazi, G. & Saidur, R. & Rismanchi, B. & Mekhilef, S., 2012. "A review on the relation between the energy and exergy efficiency analysis and the technical characteristic of the renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3131-3135.
    8. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    9. BoroumandJazi, G. & Rismanchi, B. & Saidur, R., 2013. "A review on exergy analysis of industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 198-203.
    10. Xiao Chen & Yongquan Wen & Nanyang Li, 2016. "Energy Efficiency and Sustainability Evaluation of Space and Water Heating in Urban Residential Buildings of the Hot Summer and Cold Winter Zone in China," Sustainability, MDPI, vol. 8(10), pages 1-14, September.
    11. Li, Sheng & Jin, Hongguang & Gao, Lin & Zhang, Xiaosong, 2014. "Exergy analysis and the energy saving mechanism for coal to synthetic/substitute natural gas and power cogeneration system without and with CO2 capture," Applied Energy, Elsevier, vol. 130(C), pages 552-561.
    12. Edyta Ropuszyńska-Surma & Magdalena Węglarz, 2016. "Residential electricity consumption in Poland," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(3), pages 69-82.
    13. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    14. Shen, Pengyuan & Yang, Biao, 2020. "Projecting Texas energy use for residential sector under future climate and urbanization scenarios: A bottom-up method based on twenty-year regional energy use data," Energy, Elsevier, vol. 193(C).
    15. Li, Jiaxi & Wang, Dan & Jia, Hongjie & Lei, Yang & Zhou, Tianshuo & Guo, Ying, 2022. "Mechanism analysis and unified calculation model of exergy flow distribution in regional integrated energy system," Applied Energy, Elsevier, vol. 324(C).
    16. Radwan A. Almasri & A. F. Almarshoud & Hanafy M. Omar & Khaled Khodary Esmaeil & Mohammed Alshitawi, 2020. "Exergy and Economic Analysis of Energy Consumption in the Residential Sector of the Qassim Region in the Kingdom of Saudi Arabia," Sustainability, MDPI, vol. 12(7), pages 1-20, March.
    17. Whiting, Kai & Carmona, Luis Gabriel & Sousa, Tânia, 2017. "A review of the use of exergy to evaluate the sustainability of fossil fuels and non-fuel mineral depletion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 202-211.
    18. Saidur, R. & Ahamed, J.U. & Masjuki, H.H., 2010. "Energy, exergy and economic analysis of industrial boilers," Energy Policy, Elsevier, vol. 38(5), pages 2188-2197, May.
    19. Safarzadeh, Soroush & Rasti-Barzoki, Morteza, 2019. "A game theoretic approach for assessing residential energy-efficiency program considering rebound, consumer behavior, and government policies," Applied Energy, Elsevier, vol. 233, pages 44-61.
    20. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1035-:d:738573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.