To enhance energy conservation, indoor comfort, and mitigate greenhouse gas emissions in buildings, the design of glazed facades and window systems has seen substantial improvements. These enhancements result in increased thermal resistance while maintaining access to daylight and incorporating the use of renewable energy. Some of these glazing systems possess complex structures and PV cells, which present challenges in characterising their thermal, optical, and electrical properties for utilisation in building simulations. In this research, a comprehensive model has been developed to accurately predict the thermal, optical, and electrical properties of complex PV glazing systems, and a workflow has been developed to yield detailed thermal and energy performance predictions of these systems when applied to buildings. Using this approach, the thermal properties of complex PV glazing systems are obtained from a validated Computational Fluid Dynamics (CFD) combined ray-tracing model. The recursion algorithm, along with ray-tracing calculations, is used to determine their solar-optical properties. Additionally, a PV modelling algorithm has been developed to estimate their power output. All of these properties can be integrated into building simulation software, such as EnergyPlus, to assess the thermal and energy performance (e.g., solar heat gain coefficient and power output) of the complex PV glazing system when applied to a building. In this study, a Crossed Compound Parabolic Concentrator Photovoltaic (CCPC-PV) window is selected as an example of the complex PV glazing system, and a case study is conducted to investigate the annual energy performance (heating, cooling, lighting and power generation) of a typical cellular office room using the CCPC-PV window. The results demonstrate that the comprehensive model, simulating the CCPC-PV window within building simulation software, accurately characterises its thermal, optical, and electrical properties under London’s climatic conditions. This high level of accuracy, with deviations of less than 5%, is of significant importance when simulating building energy performance with advanced glazing systems. Furthermore, the CCPC-PV window is more suitable for installation with a larger window-to-wall ratio (e.g., 64%), resulting in a 56.86% energy-saving percentage when compared to a similarly structured double-glazed window under London climate conditions.

中文翻译：

---

开发一种综合方法，将复杂门窗幕墙系统的热、光学和电气特性集成到建筑仿真软件中，以实现建筑性能表征


为了提高节能、室内舒适度并减少建筑物的温室气体排放，玻璃幕墙和窗户系统的设计得到了实质性的改进。这些增强功能提高了热阻，同时保持了日光并结合了可再生能源的使用。其中一些玻璃系统具有复杂的结构和 PV 电池，这给表征其热、光学和电气特性以用于建筑模拟带来了挑战。在这项研究中，已经开发了一个全面的模型来准确预测复杂 PV 玻璃系统的热、光学和电气特性，并且已经开发了一个工作流程，以产生这些系统在应用于建筑物时的详细热能和能源性能预测。使用这种方法，复杂光伏玻璃系统的热特性是从经过验证的计算流体动力学 （CFD） 组合光线追踪模型中获得的。递归算法以及光线追踪计算用于确定它们的太阳光特性。此外，还开发了一种光伏建模算法来估计它们的功率输出。所有这些特性都可以集成到建筑模拟软件（如 EnergyPlus）中，以评估复杂光伏玻璃系统在应用于建筑物时的热能和能源性能（例如，太阳热增益系数和功率输出）。在本研究中，选择交叉复合抛物面聚光光伏 （CCPC-PV） 窗口作为复杂光伏玻璃系统的示例，并进行了案例研究，以调查使用 CCPC-PV 窗口的典型蜂窝式办公室的年能源性能（加热、冷却、照明和发电）。 结果表明，在建筑仿真软件中模拟 CCPC-PV 窗口的综合模型准确地表征了伦敦气候条件下的热、光学和电气特性。这种高精度（偏差小于 5%）在使用高级玻璃系统模拟建筑能源性能时非常重要。此外，CCPC-PV 窗更适合安装具有较大窗墙比（例如 64%）的窗户，与伦敦气候条件下类似结构的双层玻璃窗相比，节能百分比为 56.86%。