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A Decentralized Approach on the Energy Management in Buildings

机译:建筑物能源管理的分散方法

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摘要

Buildings are accounted to consume around 40% of the total energy in Europe and USA, even more – up to 60% - in Canada and Scandinavian countries, and the rate of growth of the number of buildings follows the growth of the population. In this context, there is a vast literature dedicated to finding solutions for saving energy through innovative control systems of the heating, ventilation and lighting in residential and commercial buildings. This paper proposes a solution for controlling the HVAC system in small and medium size buildings by means of a distributed neural network, implemented using low cost microcontrollers, connected with passive infrared (PIR) motion detectors. This network is capable to detect and learn user occupancy and activity patterns, relevant for the energy management of the building. Unlike other similar systems, in our approach the energy management is entirely decentralized, relying exclusively on the above mentioned distributed ANN.
机译:在欧洲和美国,建筑物消耗的能源约占总能源的40%,在加拿大和斯堪的纳维亚国家中,消耗的能源甚至更多,高达60%,并且建筑物数量的增长率跟随人口的增长。在这种情况下,有大量文献致力于通过住宅和商业建筑中加热,通风和照明的创新控制系统来寻找节省能源的解决方案。本文提出了一种通过分布式神经网络控制中小型建筑HVAC系统的解决方案,该网络使用低成本微控制器实现,并与无源红外(PIR)运动检测器连接。该网络能够检测和学习与建筑物的能量管理有关的用户占用和活动模式。与其他类似系统不同,在我们的方法中,能源管理是完全分散的,仅依赖于上述分布式ANN。

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