Boris Savkovic,Buildingiq
加热,通风和空调(HVAC)系统调节大型建筑物(办公大楼,医院,购物中心,赌场等)的内部温度和湿度的系统占全球总能耗的约占30%。HVAC系统具有高效,从而导致不必要的能量浪费。This inefficiency stems from the fact that most HVAC control systems are passive and do not actively and predictively take into account changing weather patterns, weather forecasts, variable energy costs and tariffs, and the underlying building thermal properties in order to optimally control and regulate the building’s internal temperature and humidity so as to minimize total energy consumption.
In collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency, BuildingIQ has developed the first and only cloud-based software, employing sophisticated big data machine learning methods that continuously optimize HVAC performance in real time for minimum energy consumption, while ensuring maximum comfort for building occupants. The key advantage of this industry-leading software is that it seamlessly interfaces with current building control systems, requiring little to no capital investment for deployment within most existing building control systems. In addition to seamless integration, the software also delivers results for clients, generally achieving energy savings of 10–25% on HVAC operations, depending on the underlying building and HVAC dynamics.
此演示文稿给出了问题,实现,节能的基本纲要,以及将研发转化为实践的挑战,以及如何Matlab®用于基本算法的基本算法和基于建筑云的其余系统的接口。
记录:2015年3月25日
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