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

Abbas Javed, Hadi Larijani, Ali Ahmadinia, Rohinton Emmanuel, Mike Mannion, Des Gibson

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    Abstract

    Building Energy Management Systems (BEMS) monitor and control the Heating Ventilation and Air Conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless Sensor Networks (WSN) 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 work, 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:base station, sensor nodes, and 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%.
    Original languageEnglish
    Pages (from-to)393 - 403
    Number of pages11
    JournalIEEE Internet of Things Journal
    Volume4
    Issue number2
    Early online date9 Nov 2016
    DOIs
    Publication statusPublished - Apr 2017

    Fingerprint

    Sensor nodes
    Air conditioning
    Ventilation
    Neural networks
    Heating
    Controllers
    Energy management systems
    Base stations
    Wireless sensor networks
    Air conditioning ducts
    Processing
    Reed relays
    Air intakes
    Atmospheric humidity
    Actuators
    Cooling
    Data storage equipment
    Sensors
    Internet of things

    Keywords

    • random neural network
    • Wireless Sensor Networks
    • cloud computing

    Cite this

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    title = "Design and implementation of cloud enabled random neural network based decentralized smart controller with intelligent sensor nodes for HVAC",
    abstract = "Building Energy Management Systems (BEMS) monitor and control the Heating Ventilation and Air Conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless Sensor Networks (WSN) 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 work, 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:base station, sensor nodes, and 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{\%}.",
    keywords = "random neural network, Wireless Sensor Networks , cloud computing",
    author = "Abbas Javed and Hadi Larijani and Ali Ahmadinia and Rohinton Emmanuel and Mike Mannion and Des Gibson",
    note = "AAM: uploaded 10-1-16 Acceptance date on file ET Funding note: This work was supported in part by Innovate U.K. and in part by CENSIS U.K.",
    year = "2017",
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    doi = "10.1109/JIOT.2016.2627403",
    language = "English",
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    pages = "393 -- 403",
    journal = "IEEE Internet of Things Journal",
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    TY - JOUR

    T1 - Design and implementation of cloud enabled random neural network based decentralized smart controller with intelligent sensor nodes for HVAC

    AU - Javed, Abbas

    AU - Larijani, Hadi

    AU - Ahmadinia, Ali

    AU - Emmanuel, Rohinton

    AU - Mannion, Mike

    AU - Gibson, Des

    N1 - AAM: uploaded 10-1-16 Acceptance date on file ET Funding note: This work was supported in part by Innovate U.K. and in part by CENSIS U.K.

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    Y1 - 2017/4

    N2 - Building Energy Management Systems (BEMS) monitor and control the Heating Ventilation and Air Conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless Sensor Networks (WSN) 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 work, 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:base station, sensor nodes, and 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%.

    AB - Building Energy Management Systems (BEMS) monitor and control the Heating Ventilation and Air Conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless Sensor Networks (WSN) 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 work, 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:base station, sensor nodes, and 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%.

    KW - random neural network

    KW - Wireless Sensor Networks

    KW - cloud computing

    U2 - 10.1109/JIOT.2016.2627403

    DO - 10.1109/JIOT.2016.2627403

    M3 - Article

    VL - 4

    SP - 393

    EP - 403

    JO - IEEE Internet of Things Journal

    JF - IEEE Internet of Things Journal

    SN - 2327-4662

    IS - 2

    ER -