IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v297y2024ics0360544224009320.html
   My bibliography  Save this article

Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach

Author

Listed:
  • Sulaiman, Mohd Herwan
  • Mustaffa, Zuriani

Abstract

Chiller systems hold a critical role in upholding comfort and energy efficiency within commercial buildings. Precise prediction of chiller energy consumption is imperative for operational optimization and the reduction of energy expenditures. This paper introduces an innovative methodology that integrates deep learning (DL), specifically Fixed Forward Neural Networks (FFNN), with Teaching-Learning-Based Optimization (TLBO) to enhance the accuracy of chiller energy consumption forecasts. Drawing on a diverse dataset from a commercial building, encompassing vital input parameters such as Chilled Water Rate, Building Load, Cooling Water Temperature, Humidity, and Dew Point, the study conducts a comprehensive comparison of metaheuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Harmony Search Algorithm (HSA), Differential Evolution (DE), Ant Colony Optimization (ACO), and the latest RIME algorithm). TLBO's adept navigation of the intricate parameter space of DL yields highly precise predictions for chiller energy consumption. The study's outcomes underscore TLBO's potential, along with other metaheuristics, in optimizing DL and refining energy management practices in commercial buildings. This research significantly contributes to the evolving discourse on the symbiosis between DL, particularly FFNNs, and metaheuristic optimization, offering a robust framework for chiller energy consumption prediction, thereby advancing sustainability and cost-effectiveness in building operations.

Suggested Citation

  • Sulaiman, Mohd Herwan & Mustaffa, Zuriani, 2024. "Chiller energy prediction in commercial building: A metaheuristic-Enhanced deep learning approach," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009320
    DOI: 10.1016/j.energy.2024.131159
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224009320
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131159?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    2. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    3. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    4. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    5. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).
    6. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    7. Li, Ze & Guo, Junfei & Gao, Xinyu & Yang, Xiaohu & He, Ya-Ling, 2023. "A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers," Applied Energy, Elsevier, vol. 349(C).
    8. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
    9. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    10. Ikeda, Shintaro & Nagai, Tatsuo, 2021. "A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems," Applied Energy, Elsevier, vol. 289(C).
    11. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Li, Pengcheng & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems," Energy, Elsevier, vol. 280(C).
    12. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    13. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Chen, Siliang & Ge, Wei & Liang, Xinbin & Jin, Xinqiao & Du, Zhimin, 2024. "Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system," Applied Energy, Elsevier, vol. 353(PB).
    3. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    4. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    5. Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
    6. Ren, Zhengxiong & Han, Hua & Cui, Xiaoyu & Lu, Hailong & Luo, Mingwen, 2023. "Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios," Energy, Elsevier, vol. 279(C).
    7. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    8. Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).
    9. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    10. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    11. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    12. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
    13. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    14. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    15. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    16. Feng, Yanxiao & Liu, Shichao & Wang, Julian & Yang, Jing & Jao, Ying-Ling & Wang, Nan, 2022. "Data-driven personal thermal comfort prediction: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    17. Amjad Hudaib & Mohammad Khanafseh & Ola Surakhi, 2018. "An Improved Version of K-medoid Algorithm using CRO," Modern Applied Science, Canadian Center of Science and Education, vol. 12(2), pages 116-116, February.
    18. Liao, Tianjun & Stützle, Thomas & Montes de Oca, Marco A. & Dorigo, Marco, 2014. "A unified ant colony optimization algorithm for continuous optimization," European Journal of Operational Research, Elsevier, vol. 234(3), pages 597-609.
    19. Ali Sardar Shahraki & Mohim Tash & Tommaso Caloiero & Ommolbanin Bazrafshan, 2024. "Optimal Allocation of Water Resources Using Agro-Economic Development and Colony Optimization Algorithm," Sustainability, MDPI, vol. 16(13), pages 1-18, July.
    20. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(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:eee:energy:v:297:y:2024:i:c:s0360544224009320. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.