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

光伏系统 工艺工程 持续性 温室气体 工程类 系统工程 环境科学 计算机科学 生态学 生物 电气工程
作者
Yuekuan Zhou,Siqian Zheng
出处
期刊:Renewable & Sustainable Energy Reviews [Elsevier BV]
卷期号:192: 114184-114184 被引量:15
标识
DOI:10.1016/j.rser.2023.114184
摘要

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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