IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v307y2024ics036054422402214x.html
   My bibliography  Save this article

Energy efficiency and indoor thermal comfort of railway carriages: Development of an innovative passenger-centric-control framework for HVAC systems

Author

Listed:
  • Buonomano, A.
  • Forzano, C.
  • Giuzio, G.F.
  • Palombo, A.
  • Russo, G.

Abstract

The railway sector's energy impact is increasing, and this trend is expected to persist over the next decade. Particularly, the energy consumption of Heating Ventilation, and Air Conditioning (HVAC) systems is significant among various services provided on board (30 % of total energy requirements) due to the rising attention to passengers' thermal comfort. In this framework, this research proposes an innovative method for enhancing indoor thermal comfort while also studying the associated energy consequences. It consists of an advanced passenger-centric-control framework for HVAC systems that prioritizes comfort, utilising optimized indoor air setpoints, which surpasses the rule-based control logic proposed by the existing standards. To analyse the proposed control framework, a novel mathematical model for the energy, economic, and environmental performance analyses of train HVAC systems, which can incorporate weather data associated with actual railway paths, is developed and validated. To prove the potential of the proposed method, a proof-of-concept analysis is conducted. In particular, for an existing railway coach operating in Italy, the standard and the innovative control logic are tested and compared. The results show an interesting thermal comfort hours increase (≃+1000 h) and energy savings (−27 % yearly), proving the benefits potentially achievable by adopting the proposed method.

Suggested Citation

  • Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Russo, G., 2024. "Energy efficiency and indoor thermal comfort of railway carriages: Development of an innovative passenger-centric-control framework for HVAC systems," Energy, Elsevier, vol. 307(C).
  • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s036054422402214x
    DOI: 10.1016/j.energy.2024.132440
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422402214X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.132440?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. Shao, Z. & Wang, Z.G. & Poredoš, P. & Ge, T.S. & Wang, R.Z., 2023. "Highly efficient desiccant-coated heat exchanger-based heat pump to decarbonize rail transportation," Energy, Elsevier, vol. 271(C).
    2. Giovanni Barone & Annamaria Buonomano & Cesare Forzano & Giovanni Francesco Giuzio & Adolfo Palombo, 2021. "Improving the Efficiency of Maritime Infrastructures through a BIM-Based Building Energy Modelling Approach: A Case Study in Naples, Italy," Energies, MDPI, vol. 14(16), pages 1-24, August.
    3. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2022. "Energy, economic, and environmental impacts of enhanced ventilation strategies on railway coaches to reduce Covid-19 contagion risks," Energy, Elsevier, vol. 256(C).
    4. Barone, Giovanni & Buonomano, Annamaria & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2023. "Towards zero energy infrastructure buildings: optimal design of envelope and cooling system," Energy, Elsevier, vol. 279(C).
    5. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    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. Buonomano, A. & Forzano, C. & Mongibello, L. & Palombo, A. & Russo, G., 2024. "Optimising low-temperature district heating networks: A simulation-based approach with experimental verification," Energy, Elsevier, vol. 304(C).
    2. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    3. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    4. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    5. 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).
    6. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    7. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    8. Giacomo Segala & Roberto Doriguzzi-Corin & Claudio Peroni & Matteo Gerola & Domenico Siracusa, 2023. "EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation," Energies, MDPI, vol. 16(21), pages 1-28, October.
    9. Constantinos Vassiliades & Christos Minterides & Olga-Eleni Astara & Giovanni Barone & Ioannis Vardopoulos, 2023. "Socio-Economic Barriers to Adopting Energy-Saving Bioclimatic Strategies in a Mediterranean Sustainable Real Estate Setting: A Quantitative Analysis of Resident Perspectives," Energies, MDPI, vol. 16(24), pages 1-18, December.
    10. 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.
    11. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    12. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    13. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    14. Wang, Xuezheng & Dong, Bing, 2024. "Long-term experimental evaluation and comparison of advanced controls for HVAC systems," Applied Energy, Elsevier, vol. 371(C).
    15. Farizal Farizal & Muhammad Aqil Noviandri & Hanif Hamdani, 2024. "Sustainability Development through a Nearly Zero Energy Building Implementation Case: An Office Building in South Jakarta," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    16. Angizeh, Farhad & Ghofrani, Ali & Zaidan, Esmat & Jafari, Mohsen A., 2022. "Adaptable scheduling of smart building communities with thermal mapping and demand flexibility," Applied Energy, Elsevier, vol. 310(C).
    17. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
    18. Jan Růžička & Jakub Veselka & Zdeněk Rudovský & Stanislav Vitásek & Petr Hájek, 2022. "BIM and Automation in Complex Building Assessment," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    19. Barone, G. & Buonomano, A. & Cipolla, G. & Forzano, C. & Giuzio, G.F. & Russo, G., 2024. "Designing aggregation criteria for end-users integration in energy communities: Energy and economic optimisation based on hybrid neural networks models," Applied Energy, Elsevier, vol. 371(C).
    20. Shan, He & Poredoš, Primož & Zou, Hao & Lv, Haotian & Wang, Ruzhu, 2023. "Perspectives for urban microenvironment sustainability enabled by decentralized water-energy-food harvesting," Energy, Elsevier, vol. 282(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:307:y:2024:i:c:s036054422402214x. 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.