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A 'big data' approach to the application of building performance simulation to improve the operational performance of large estates

机译:一种“大数据”方法在建筑性能模拟中的应用,以改善大型房地产的运营性能

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摘要

This paper derives from the ‘Hit2Gap’ project, funded under the European Union’s Horizon 2020 R&D programme (Hit2Gap 2015). The aim of the project is to reduce the gap between design intent and the operational performance of large building estates. To this end, a data exchange platform is being prototyped and tested, able to collect and store data from disparate sources and deliver subsets of these data to a range of applications (services) and end users. The ultimate aim is to identify physical interventions that could alleviate operational problems and so reduce the performance gap. This paper deals with the application of building performance simulation (BPS) within the context of data exchange platform and specifically, the delivery of an input model, its automated calibration and use in areas such as HVAC system fault detection and diagnosis, upgrade options appraisal, indoor environment quality improvement, demand reduction, renewable energy systems integration, control system refinement, and regulatory compliance. The paper summarises the Hit2Gap architecture, the procedure for the automatic calibration of BPS models and automated performance assessments.
机译:本文来自“ Hit2Gap”项目,该项目由欧盟的Horizo​​n 2020 R&D计划资助(Hit2Gap 2015)。该项目的目的是缩小大型住宅区的设计意图与运营性能之间的差距。为此,正在对数据交换平台进行原型设计和测试,该平台能够收集和存储来自不同来源的数据,并将这些数据的子集传递给一系列应用程序(服务)和最终用户。最终目的是确定可以减轻操作问题并缩小性能差距的物理干预措施。本文讨论了建筑性能模拟(BPS)在数据交换平台范围内的应用,特别是输入模型的交付,其自动校准和在HVAC系统故障检测和诊断,升级选项评估,室内环境质量改善,需求减少,可再生能源系统集成,控制系统完善和法规遵从性。本文总结了Hit2Gap体系结构,BPS模型自动校准和自动性能评估的过程。

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