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首页> 外文期刊>Internet of Things Journal, IEEE >Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC
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Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC

机译：基于云的随机神经网络智能空调节点分散智能控制器的设计与实现

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Building energy management systems (BEMSs) monitor and control the heating ventilation and air conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless sensor networks (WSNs) have become the integral part of BEMS at the initial implementation phase or latter when retro fitting is required to upgrade older buildings. WSN enabled BEMS, however, have several challenges which are managing data, controllers, actuators, intelligence, and power usage of wireless components (which might be battery powered). The wireless sensor nodes have limited processing power and memory for embedding intelligence in the sensor nodes. In this paper, we present a random neural network (RNN)-based smart controller on a Internet of Things (IoT) platform integrated with cloud processing for training the RNN which has been implemented and tested in an environment chamber. The IoT platform is modular and not limited to but has several sensors for measuring temperature, humidity, inlet air coming from the HVAC duct and PIR. The smart RNN controller has three main components: 1) base station; 2) sensor nodes; and 3) the cloud with embedded intelligence on each component for different tasks. This IoT platform is integrated with cloud processing for training the RNN. The RNN-based occupancy estimator is embedded in sensor node which estimates the number of occupants inside the room and sends this information to the base station. The base station is embedded with RNN models to control the HVAC on the basis of setpoints for heating and cooling. The HVAC of the environment chamber consumes 27.12% less energy with smart controller as compared to simple rule-based controllers. The occupancy estimation time is reduced by our proposed hybrid algorithm for occupancy estimation that combines RNN-based occupancy estimator with door sensor node (equipped with PIR and magnetic reed switch). The results show that accuracy of hybrid RNN occupancy estimator is 88%.
机译：除许多其他建筑系统和公用事业外，建筑能源管理系统（BEMS）监视和控制建筑物的采暖通风和空调（HVAC）。无线传感器网络（WSN）在最初的实施阶段已成为BEMS不可或缺的一部分，而在需要对旧建筑物进行升级改造时则更是如此。但是，启用了WSN的BEMS面临一些挑战，包括管理数据，控制器，执行器，智能以及无线组件（可能由电池供电）的功耗。无线传感器节点的处理能力和内存有限，无法在传感器节点中嵌入智能。在本文中，我们提出了一种在物联网（IoT）平台上与云处理集成的基于随机神经网络（RNN）的智能控制器，用于训练已在环境室内进行了测试的RNN。物联网平台是模块化的，不仅限于此，还具有多个传感器，用于测量温度，湿度，来自HVAC管道和PIR的进气。智能RNN控制器具有三个主要组件：1）基站； 2）传感器节点； 3）在每个组件上都具有嵌入式智能的云，以完成不同的任务。该物联网平台与云处理集成在一起，用于训练RNN。基于RNN的占用估算器嵌入在传感器节点中，该传感器节点估算房间内的占用人数并将此信息发送到基站。基站内嵌有RNN模型，可根据加热和冷却的设定值控制HVAC。与简单的基于规则的控制器相比，使用智能控制器的环境室的HVAC能耗减少了27.12％。通过将基于RNN的占用估算器与门传感器节点（配备PIR和磁簧开关）相结合的建议的占用估算混合算法，可以减少占用估算时间。结果表明，混合RNN占用估计器的准确性为88％。

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