通过网络地理信息系统实现智能供暖、通风、空调和制冷

Kian Hariri Asli, K. Asli
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引用次数: 0

摘要

目标:目前,供暖、通风、空调和制冷(HVAC&R)系统占能源消耗的很大一部分。供暖、通风、空调和制冷系统是建筑物中能耗最大的部分,因此必须优化能耗,提高全球节能水平。方法基于网络的地理信息系统(GIS)实现了全球空间数据的无缝共享,可通过万维网随时随地访问。一套远程读取网络传感器、先进的调制解调器和数据记录器促进了暖通空调与制冷设施地理数据库的互联互通。遥感技术与物联网的整合,以地理信息系统为基础,建立了一个专门用于节约能源的控制回路。这种方法是控制科学中的一个开创性概念,为加强设计、维护和能源管理实践提供了巨大潜力。它赋予能源用户对其能源消耗进行实时控制的能力,在这一领域取得了重大进展。成果:在这项工作中,暖通空调与制冷控制模型与基于网络的地理信息系统相结合,显示出符合计算方法的回归数学分析具有预测能源消耗和评估能源损失的能力。结论在回归分析中发现,不满意百分比的 P 值为 0.991,能源使用强度的 P 值为 0.977,数据包络分析效率的 P 值为 0.962。此外,曲线估算表明,在回归分析过程中使用了幂函数。
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Smart Heating, Ventilating, Air-conditioning and Refrigeration by Web-based Geographic Information System
Objective: Heating, ventilating, air-conditioning and refrigeration (HVAC&R) systems currently account for a significant portion of energy consumption. The HVAC&R system contributes the largest energy consumption in a building, so it is essential to optimize energy consumption to improve energy saving worldwide. Methods: The web-based geographic information system (GIS) enables the seamless sharing of spatial data across the globe, accessible anytime and anywhere via the World Wide Web. The set of remote reading networked sensors, advanced modems, and data loggers facilitate the intercommunication for the geodatabase of HVAC&R’s facilities. The integration of remote sensing technology and the Internet of Things, grounded in GIS establishes a control loop dedicated to energy conservation. This method is a pioneering concept in control science, offering significant potential for enhancing design, maintenance, and energy management practices. It empowers energy users with real-time control over their energy consumption, making a substantial advancement in this field. Results: In this work, the model of HVAC&R control in context with web-based GIS showed that the regression mathematical analysis in compliance with the computational method holds the capacity to predict energy consumption and evaluate energy loss. Conclusion: In regression analysis, the P was found to be 0.991 for the percentage of dissatisfaction, 0.977 for energy use intensity, and 0.962 for data envelopment analysis efficiency. Additionally, the curve estimation showed that the power function was utilized in regression analysis processes.
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