Design and implementation of a cloud enabled random neural network-based decentralized smart controller with intelligent sensor nodes for HVAC

A. Javed, H. Larijani, A. Ahmadinia, R. Emmanuel, M. Mannion, D. Gibson

Research output: Contribution to journalArticle

23 Citations (Scopus)
118 Downloads (Pure)

Abstract

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 occupanc- estimator is 88%.
Original languageEnglish
Pages (from-to)393-403
Number of pages11
JournalIEEE Internet of Things Journal
Volume4
Issue number2
DOIs
Publication statusPublished - 9 Nov 2016

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Keywords

  • HVAC
  • cloud computing
  • control engineering computing
  • decentralised control
  • neural nets
  • power engineering computing
  • BEMS
  • Internet of Things
  • IoT platform
  • RNN-based occupancy estimator
  • RNN-based smart controller
  • WSN
  • building energy management systems
  • cloud enabled random neural network
  • decentralized smart controller
  • door sensor node
  • heating ventilation and air conditioning
  • intelligent sensor nodes
  • magnetic reed switch
  • wireless sensor networks
  • Base stations
  • Buildings
  • Cloud computing
  • Computational modeling
  • Estimation
  • Training
  • Wireless sensor networks
  • Decentralized control
  • Internet of Things (IoT)
  • heating ventilation and air conditioning (HVAC) control
  • intelligent sensors
  • occupancy estimation
  • random neural networks (RNNs)
  • smart homes
  • supervised learning
  • thermal comfort
  • wireless sensor networks (WSNs)

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