Energy efficiency strategies based on daylight-artificial light integration have grown exponentially in recent years. Taking into account the dynamics to be considered for control and the dependence on natural and occupancy factors, it is better to use a test workbench prior to setting up the final control scheme. This work describes a climate model based test workbench for the real time testing of the control of luminaires and window blinds in a daylight-artificial light integrated scheme. The established climate model based control scheme suitable for the optimum integration of visual comfort, thermal comfort, and energy consumption can be tested for any ecological conditions. The input irradiance from a BF5 sensor, the internal temperature from a Micro DAQ logger, the occupancy and photo sensors associated with the luminaire all provide input data for the test workbench. A fuzzy logic based motorized window blind controller and look-up table based dimming of LED luminaires are used to set the required illuminance with reduced load on the heating, ventilation, and air conditioning system. The anticipated synergetic effects of the test workbench have been validated using real time climate data. The test work bench is established on a Labview platform and developed as a standalone system using myRIO.

ZigBee standard is the popular communication protocol for wireless sensor networks (WSN) and Internet of Things (IoT) networks. An energy efficient WSN technology is a good choice for an IoT based lighting control technology. This article is a comparative analysis for finding the quality of service parameters provided by the different topologies of ZigBee for a wireless networked lighting control system. In order to estimate the energy consumption in ZigBee topologies for lighting automation, this work analyses the star, mesh, and tree topology-based WSN with two routing protocols Adhoc on-demand distance vector (AODV) and dynamic source routing (DSR). Lighting automation using wireless control networks with sensor-actuator nodes in a laboratory is considered as the test scenario. The applicability of ZigBee topologies for IoT-based lighting automation is discussed by the evaluation of performance parameters like average jitter, throughput, end-to-end delay, and energy model.

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Buildings consume tremendous energy for the improvement of living and working conditions. Control of daylight-artificial light has the potential to improve energy performance and occupant comfort in buildings. This research proposes an intelligent generalized ensemble learning technique to develop a novel control strategy for Venetian-blind positioning (up-down movement with static slat angle of 45 • ) of different window orientations. The proposed model helps to maintain occupant comfort and energy saving in a commercial building. The performance of the ensemble learning approach compared against Gaussian process regression, support vector regression and artificial neural network using conventional statistical indicators. Finally, the proposed data-driven model implemented in a real-time Labview-myRIO platform for the experimental validation. The data-driven model is compared with the baseline model and with the uncontrolled blind condition in terms of daylight glare, and energy consumption of lighting and air-conditioning system in the building. The data-driven model is derived using two years of data collected from a fuzzy-based daylight-artificial light integrated scheme. The blind position providing reduced energy consumption and daylight glare along with setpoint illuminance and temperature are validated. A high dynamic range image with EVALGLARE software used to verify the visual comfort based on daylight glare probability. While evaluating the overall energy savings, the ensemble learning model consumes 17% less power than the uncontrolled system and 15% less power than the baseline system. Here, though we are not controlling the air-conditioning system, the experimental validation confirmed that the air-conditioning system significantly reduces its energy consumption.INDEX TERMS Window blind control, data-driven models, ensemble learning, bayesian optimization, daylight glare, labview, myRIO, energy comparison, lighting control, air-conditioning.

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.