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PV-PCM integration in glazed building. Co-simulation and genetic optimization study

机译:玻璃建筑中的PV-PCM集成。协同仿真与遗传优化研究

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The study describes a multi-objective optimization algorithm for an innovative integration of forced ventilated PV-PCM modules in glazed facade buildings: the aim is to identify and optimize the parameters that most affect thermal and energy performances. 1-D model, finite difference method FDM, thermal resistances technique and enthalpy method were applied to describe different facade solutions and transient thermal performance of PCM. The coupling between the PV-PCM facade code implemented in MATLAB and the TRNSYS software was developed to estimate the dynamic thermal energy profiles. An exploratory step has also been considered prior to the optimization algorithm: it evaluates the energy profiles before and after the application of PCM to PV module integrated in glazed building. The optimization analysis investigate parameters such as ventilation flow rates and time schedule to obtain the best combination suiting the PCM performance and external-internal loads. A group of solution were identified on the Pareto front. Savings in thermal loads for the best individual reached 26.4% while the best in temperature increment in operating temperatures was recorded as 6.8% comparing to the design set temperature. (C) 2017 Elsevier Ltd. All rights reserved.
机译:这项研究描述了一种多目标优化算法,用于在玻璃幕墙建筑中创新集成强制通风的PV-PCM模块:目的是识别和优化对热量和能源性能影响最大的参数。用一维模型,有限差分法FDM,热阻技术和焓法描述了不同的立面解决方案和PCM的瞬态热性能。开发了在MATLAB中实现的PV-PCM外墙代码与TRNSYS软件之间的耦合,以估算动态热能曲线。在优化算法之前还考虑了一个探索性步骤:在将PCM应用于玻璃建筑中集成的PV模块之前和之后,评估能量分布。优化分析会研究通风流量和时间表等参数,以获得适合PCM性能和内外部负载的最佳组合。在帕累托前沿确定了一组解决方案。与设计设定温度相比,最佳人员的热负荷节省达到26.4%,而工作温度的最佳温度增量记录为6.8%。 (C)2017 Elsevier Ltd.保留所有权利。

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