This paper presents a data analytics model-based predictive control approach for a daylight–artificial light integrated scheme. The essential data are collected from an automated test room with dimmable LED luminaires and motorized Venetian blinds. This study considered machine learning techniques to develop a novel control strategy for all the four-window orientations for maintaining comfort and energy conservation. The shades are operated one at a time, and the annual data collected were used to develop the predictive models. The irradiance, altitude, temperature and daylight on the window are the predictors, and the blind position is the response variable to establish the models for the windows on all four sides of the test room. The standard support vector regression, Bayesian support vector regression and Gaussian process regression models are analysed in comparison with the baseline model. The luminaire dimming control signals generated using the predicted optimum blind position and exterior illuminance based on a building information illuminance model is commissioned for a given room. This approach mainly concentrated on the implementation of an industrial-level product by reducing the computational complexity of the rule-based blind positioning system. At present, the models are in a reconfigurable embedded WiFi-enabled operating system.
Background: The lighting researchers are keenly looking for the huge benefits of the internet of things on an open platform which provides the cost gains in addition to other environmental benefits. Connected systems interact with the software and analyse real-time building conditions, and feed information into the building controls network. Methods: This paper presents a wireless networked system for lighting control in buildings which connect the power of the Internet of Things. After analysing the ZigBee network on QualNet v7.4, a Digi Mesh network was set up using XBee modules using the XBee Configuration and Test Utility [XCTU] Software v6.3.11. The ThingSpeak cloud platform along with MATLAB 2017b provides the necessary cloud support to enable this network to communicate over the internet. The results indicate that the XBee S2C module functioning in the API mode when flashed with the DigiMesh firmware offers the best option for forming a self-healing mesh network. An aggregator node acts as an information sink and collects the sensor data from all the sensor nodes and passes it on to the cloud via the Raspberry gateway. Results: The algorithm on the cloud can read this sensor data and compute the necessary Pulse Width Modulation [PWM] signals required to control the brightness of a dimmable LED luminaire. The system also takes into consideration the zone-wise occupancy in the room while computing the PWM values to be sent to the luminaires. Conclusion: The use of the concept of open platform sensors and actuators is the significance of the work.
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