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

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  • 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
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    References listed on IDEAS

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    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. 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).
    4. 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).
    5. 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).
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