IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2020i1p122-d469551.html
   My bibliography  Save this article

Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses

Author

Listed:
  • Sungjin Lee

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
    Department of Architecture and Architectural Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Soo Cho

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Seo-Hoon Kim

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Jonghun Kim

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Suyong Chae

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Hakgeun Jeong

    (Korea Institute of Energy Research, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Taeyeon Kim

    (Department of Architecture and Architectural Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy ( R 2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy ( Cv ( RMSE )) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

Suggested Citation

  • Sungjin Lee & Soo Cho & Seo-Hoon Kim & Jonghun Kim & Suyong Chae & Hakgeun Jeong & Taeyeon Kim, 2020. "Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses," Energies, MDPI, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:122-:d:469551
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/122/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/1/122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    2. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    3. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Ciulla, G. & D'Amico, A. & Lo Brano, V. & Traverso, M., 2019. "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level," Energy, Elsevier, vol. 176(C), pages 380-391.
    5. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    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. Younhee Choi & Doosam Song & Sungmin Yoon & Junemo Koo, 2021. "Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads," Energies, MDPI, vol. 14(2), pages 1-23, January.
    2. Jing Xu & Ren Zhang & Yangjun Wang & Hengqian Yan & Quanhong Liu & Yutong Guo & Yongcun Ren, 2022. "Assessing China’s Investment Risk of the Maritime Silk Road: A Model Based on Multiple Machine Learning Methods," Energies, MDPI, vol. 15(16), pages 1-15, August.

    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. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Sun, Yuanyuan & Xie, Xiangmin & Wang, Qingyan & Zhang, Linghan & Li, Yahui & Jin, Zongshuai, 2020. "A bottom-up approach to evaluate the harmonics and power of home appliances in residential areas," Applied Energy, Elsevier, vol. 259(C).
    4. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    5. Melillo, Andreas & Durrer, Roman & Worlitschek, Jörg & Schütz, Philipp, 2020. "First results of remote building characterisation based on smart meter measurement data," Energy, Elsevier, vol. 200(C).
    6. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    7. Chou, Jui-Sheng & Truong, Ngoc-Son, 2019. "Cloud forecasting system for monitoring and alerting of energy use by home appliances," Applied Energy, Elsevier, vol. 249(C), pages 166-177.
    8. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    9. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    10. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    11. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    12. Yang, Yunpeng & Yang, Weixin & Chen, Hongmin & Li, Yin, 2020. "China’s energy whistleblowing and energy supervision policy: An evolutionary game perspective," Energy, Elsevier, vol. 213(C).
    13. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    14. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    15. Roberto Morcillo-Jimenez & Karel Gutiérrez-Batista & Juan Gómez-Romero, 2023. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data," Energies, MDPI, vol. 16(4), pages 1-29, February.
    16. Anubhav Kumar Pandey & Vinay Kumar Jadoun & Jayalakshmi N. Sabhahit, 2022. "Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources," Energies, MDPI, vol. 15(16), pages 1-30, August.
    17. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    18. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    19. Kostadin Yotov & Emil Hadzhikolev & Stanka Hadzhikoleva & Stoyan Cheresharov, 2022. "Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    20. R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.

    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:14:y:2020:i:1:p:122-:d:469551. 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.