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A co-simulated material-component-system-district framework for climate-adaption and sustainability transition

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  • Zhou, Yuekuan
  • Zheng, Siqian

Abstract

Due to considerable carbon emissions in building sectors, sustainability transformation is essential for power supply reliability, stability, grid-friendly interaction, and integration with e-transportation. However, building sustainability transformation requires inter-disciplinary and trans-disciplinary platforms for ‘material-component-building-district’ co-simulations and innovations. In this study, a generic methodology is proposed to comprehensively interconnect nano-scale material and energy systems in thermal transport and thermodynamics, guiding the design and operation for lifecycle sustainability, together with carbon intensity quantification and decarbonisation potential. Afterwards, a cross-scale energy simulation platform is formulated, involving nanoporous materials, innovative components, building integrations, and district energy analytics. The formulated platform can enable synthetical and comprehensive analysis on thermodynamic performances, energy performances, energy conversion and management, throughout integrated cross-disciplinary approaches by overcoming performance overestimation or underestimation of traditional single-stage approaches. The application of the platform quantifies the decreasing magnitude of energy consumption for PCM microcapsule wall, self-cleaning façade coating, clear thermal resistant cleaning glass coating, evaporative cooling & solar PV roof, volatile organic compound (VOC) absorption for indoor air quality (IAQ) control, building integrated photovoltaics (BIPVs), solar thermal collectors and 10-kW wind turbine. Afterwards, dynamic interaction between real buildings and digital twin models was realized for fast computation and prediction, labour cost and initial investment cost saving, long-term performance analysis. Both historical database and digital twin-generated database can promote the development of machine learning (ML) models, through data preparation, hyperparameter optimization, model training, testing, and validation. The proposed approach and formulated platform can enable synthetical and comprehensive analysis on building sustainability, throughout integrated cross-disciplinary approaches for 2060 carbon neutrality in China.

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  • Zhou, Yuekuan & Zheng, Siqian, 2024. "A co-simulated material-component-system-district framework for climate-adaption and sustainability transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:rensus:v:192:y:2024:i:c:s1364032123010420
    DOI: 10.1016/j.rser.2023.114184
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    1. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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