Experiments on Energy Optimization in Smart Residences

R. N. S. Cruz, H. Sampaio, R. N. Boing, Carlos Becker Westphall
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Abstract

Internet of Things (IoT) devices emerge to integrate devices (or "things") into the Internet, with limited computational resources, thus IoT networks have been integrated into the cloud and fog paradigms to mitigate these drawbacks. On the other hand, artificial intelligence (AI) techniques have shown to be efficient in several different areas, especially for data classification and prediction. In this paper, it is proposed a model of an RFID access control system with a neural network fog module that, considering the access data in a smart condominium with 300 homes, can estimate schedules and figure out when the homes are unoccupied, and by using the long sleep technique during that time, up to 9.9% of additional energy savings could be obtained. Future work can use this knowledge for developing a variety of optimizations and to improve the residents’ quality of life. The viability of this model is demonstrated by a fog network prototype.
智能住宅能源优化实验研究
物联网(IoT)设备以有限的计算资源将设备(或“事物”)集成到互联网中,因此物联网网络已集成到云和雾范式中以减轻这些缺点。另一方面,人工智能(AI)技术已经在几个不同的领域显示出效率,特别是在数据分类和预测方面。本文提出了一种具有神经网络雾模块的RFID门禁系统模型,该模型考虑了300户智能公寓的门禁数据,可以估计出时间表并计算出房屋空置时间,并在此期间使用长睡眠技术,可获得高达9.9%的额外节能。未来的工作可以利用这些知识来开发各种优化和提高居民的生活质量。通过一个雾网络原型验证了该模型的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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