IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7508-d1277134.html
   My bibliography  Save this article

A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks

Author

Listed:
  • Berny Carrera

    (Department of Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea)

  • Kwanho Kim

    (Department of Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of Korea)

Abstract

Currently, a smart city should ideally be environmentally friendly and sustainable, and energy management is one method to monitor sustainable use. This research project investigates the potential for a “smart city” to improve energy management by enabling the adoption of various types of intelligent technology to improve the energy sustainability of a city’s infrastructure and operational efficiency. In addition, the South Korean smart city region of Songdo serves as the inspiration for this case study. In the first module of the proposed framework, we place a strong emphasis on the data capabilities necessary to generate energy statistics for each of the numerous structures. In the second phase of the procedure, we employ the collected data to conduct a data analysis of the energy behavior within the microcities, from which we derive characteristics. In the third module, we construct baseline regressors to assess the proposed model’s varying degrees of efficacy. Finally, we present a method for building an energy prediction model using a deep learning regression model to solve the problem of 48-hour-ahead energy consumption forecasting. The recommended model is preferable to other models in terms of R 2 , MAE, and RMSE, according to the study’s findings.

Suggested Citation

  • Berny Carrera & Kwanho Kim, 2023. "A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks," Energies, MDPI, vol. 16(22), pages 1-24, November.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7508-:d:1277134
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7508/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/22/7508/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    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. Izabela Rojek & Dariusz Mikołajewski & Krzysztof Galas & Adrianna Piszcz, 2025. "Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities," Energies, MDPI, vol. 18(2), pages 1-19, January.

    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. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    2. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    3. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    4. Thomas Wu & Bo Wang & Dongdong Zhang & Ziwei Zhao & Hongyu Zhu, 2023. "Benchmarking Evaluation of Building Energy Consumption Based on Data Mining," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    5. Zhiyong Li & Shiping Pu & Yougen Chen & Renyong Wei, 2020. "An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA," Energies, MDPI, vol. 13(17), pages 1-24, August.
    6. Zare, Shahryar & Tavakolpour-saleh, A.R. & Aghahosseini, A. & Sangdani, M.H. & Mirshekari, Reza, 2021. "Design and optimization of Stirling engines using soft computing methods: A review," Applied Energy, Elsevier, vol. 283(C).
    7. Wang, Chuan'an & Pouramini, Somayeh, 2024. "Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room," Energy, Elsevier, vol. 292(C).
    8. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    9. Le, Son Tay & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Teodosio, Birch & Ngo, Tuan Duc, 2024. "Comparative life cycle assessment of renewable energy storage systems for net-zero buildings with varying self-sufficient ratios," Energy, Elsevier, vol. 290(C).
    10. Kirim Lee & Jinhwan Park & Sejung Jung & Wonhee Lee, 2021. "Roof Color-Based Warm Roof Evaluation in Cold Regions Using a UAV Mounted Thermal Infrared Imaging Camera," Energies, MDPI, vol. 14(20), pages 1-17, October.
    11. Le, Son Tay & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Ngo, Tuan Duc, 2024. "Techno-economic and life cycle analysis of renewable energy storage systems in buildings: The effect of uncertainty," Energy, Elsevier, vol. 307(C).
    12. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    13. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    14. Le, Tay Son & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Ngo, Tuan Duc, 2023. "Optimal sizing of renewable energy storage: A techno-economic analysis of hydrogen, battery and hybrid systems considering degradation and seasonal storage," Applied Energy, Elsevier, vol. 336(C).
    15. Wang, Guimei & Moayedi, Hossein & Thi, Quynh T. & Mirzaei, Mojtaba, 2024. "Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms," Energy, Elsevier, vol. 302(C).
    16. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
    17. Dhowmya Bhatt & Danalakshmi D & A. Hariharasudan & Marcin Lis & Marlena Grabowska, 2021. "Forecasting of Energy Demands for Smart Home Applications," Energies, MDPI, vol. 14(4), pages 1-19, February.
    18. Han, Yongming & Li, Jingze & Lou, Xiaoyi & Fan, Chenyu & Geng, Zhiqiang, 2022. "Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient," Applied Energy, Elsevier, vol. 309(C).
    19. Jeeheon Kim & Yongsug Hong & Namchul Seong & Daeung Danny Kim, 2022. "Assessment of ANN Algorithms for the Concentration Prediction of Indoor Air Pollutants in Child Daycare Centers," Energies, MDPI, vol. 15(7), pages 1-17, April.
    20. Lin, Xiaojie & Zhang, Junwei & Du-Ikonen, Liuliu & Zhong, Wei, 2023. "An infiltration load calculation model of large-space buildings based on the grand canonical ensemble theory," Energy, Elsevier, vol. 275(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:16:y:2023:i:22:p:7508-:d:1277134. 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.