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Networked Indoor Lighting Controls with Visible Light Communication Delft University of Technology Master’s Thesis in Embedded Systems Networked Indoor Lighting Controls with Visible Light Communication Kevin Warmerdam Networked Indoor Lighting Controls with Visible Light Communication Master's Thesis in Embedded Systems Embedded Software Section Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands Kevin Warmerdam 1505343 [email protected] September 10, 2015 Author Kevin Warmerdam ([email protected]) Title Networked Indoor Lighting Controls with Visible Light Communication MSc presentation September 23, 2015 Graduation Committee Prof. Dr. Koen Langendoen (chair) Delft University of Technology Dr. Zaid Al-Ars Delft University of Technology Dr. Ashish Pandharipande Philips Research Dr. Marco Zuniga Delft University of Technology Abstract Intelligent lighting systems employ dimmable luminaires, photosensors, and occupancy sensors to adapt to daylight and user presence conditions in in- door environments. By providing the illumination required for users and no more, significant energy savings can be made. The state of the art in these lighting systems currently relies on dedicated communication hardware such as radio networking modules. Additionally, the state of the art relies on pa- rameters specific for the environment to be known called the optical channel gains. Although these may be measured in a calibration step while the sys- tem is offline, occupants interacting with the environment affect the optical channel gains. Currently, such environment changes can compromise the desired control behavior of intelligent lighting systems. Visible light communication (VLC) presents an alternative to radio com- munication in networked lighting control systems. It reuses the system's luminaires as transmitters and its photosensors as receivers. This way, ded- icated communication hardware is no longer required. Furthermore, the reception of signals on the optical channel between luminaires and photo- sensors allows for the estimation of the optical channel gains. By estimating these during communication, the system becomes adaptable to changes in the environment. The proposed system is evaluated against the state of the art in radio- networked lighting control using simulations as well as an experimental testbed. The VLC-networked lighting control system is shown to be resilient against changes to the environment which the state of the art systems are compromised by. iv Preface In the field of indoor lighting, energy efficiency and user comfort are the two conflicting goals. An optimum between the two exists where the desired light is present, composed of both daylight and just the right amount of artificial light. Since it is not expected of the user to employ a dimming switch and constantly minimize the artificial light, depending on the amount of sunlight entering his or her room, indoor environments are to this day often either fully lit with the maximum power output provided by its overhead lamps or these are entirely turned off. One speculates to what degree users are even willing to switch off lights when leaving such rooms for any period of time. The desired optimum calls for the automation of lighting systems, where luminaires are dimmed based on daylight and occupancy conditions. This thesis proposes that user comfort may be guaranteed while energy costs may be minimized. The work of this thesis was done at a company, namely Philips Research in Eindhoven. The history of Philips can be traced back to the 19th century, when it began the production of incandescent lamps which would eventually give Eindhoven the identity of `Lichtstad' (City of Light). Where better to explore intelligent lighting systems for a master's thesis than here? Before the underlying challenges and novel solutions within intelligent light- ing systems are revealed, allow me to express my earnest gratitude to several parties: to Ashish Pandharipande and Marco Zuniga for their supervision from near and far, respectively; to my parents for their prolonged support which has culminated into this conclusion of my studies; and to my girl- friend Lotte, who shared the move to Eindhoven with me as well as every day since. Kevin Warmerdam Eindhoven, The Netherlands September 10, 2015 v vi Contents Preface v 1 Introduction 1 1.1 Motivation . 1 1.2 System considerations . 1 1.2.1 Sensor placement . 1 1.2.2 Lighting control algorithms . 3 1.2.3 Optical channel gain . 4 1.2.4 Visible light communication . 4 1.3 Problem statement . 5 1.4 Structure and organization . 5 1.4.1 Note on generality . 5 1.4.2 Thesis structure . 5 2 State of the art 7 2.1 Optical wireless communications . 7 2.2 Intelligent lighting . 8 2.2.1 Daylight adaptation . 8 2.2.2 Occupancy adaptation . 9 2.2.3 Networking . 9 2.2.4 Environment changes . 10 2.3 Contribution . 11 3 System model 13 3.1 Networked lighting control . 13 3.2 Visible light communication . 14 3.2.1 Modulated signal . 14 3.2.2 Message interpretation . 15 3.3 Scheduling . 17 4 Method 19 4.1 VLC link performance . 19 4.2 Estimation of control variables . 20 4.2.1 Optical channel gain extraction . 20 vii 4.2.2 Daylight estimation . 22 4.3 Control algorithm . 23 5 Results 27 5.1 Performance of VLC . 27 5.1.1 Simulation . 28 5.1.2 Experimental . 31 5.2 Performance of networked lighting control . 35 5.2.1 Simulation . 35 5.2.2 Experimental . 38 6 Conclusions and future work 45 6.1 Conclusions . 45 6.2 Future work . 46 6.2.1 Internet of Things application . 47 A Convex objective function derivation 55 viii Chapter 1 Introduction 1.1 Motivation In the commercial sector, lighting is responsible for 19% of the total en- ergy consumption [1]. Consider the multitude of buildings in existence and how their indoor lighting is regulated. On average 23% of the total elec- trical energy consumption in buildings has been shown to go to waste on poor management of occupancy conditions [2], where environments are il- luminated while no one is present. The amount of energy that is spent on environments which are already illuminated by daylight is even greater [3]. Significant costs may be saved in indoor office environments with a light- ing solution which minimizes its expended energy while satisfying users' illumination requirements. Intelligent lighting systems address these issues. Lamps, hereafter called luminaires, may be connected with sensors to de- tect both occupancy and illumination conditions. A system may be designed which, based on the input of these sensors, adapts and dims the luminaires to a desired level of output illuminance and no more. 1.2 System considerations In the following sections, several key aspects of the proposed intelligent light- ing system are introduced. They serve to illustrate concepts and challenges which are revisited in the chapters that follow. 1.2.1 Sensor placement Desired conditions of illumination within workplaces have been addressed in European standards [4]. Minimum levels of illuminance (measured in lux) on the workplane level, for instance on desks, are defined in these stan- dards based on whether the region is occupied or unoccupied. Note that for the purposes of an intelligent lighting system, it is impractical to mount 1 photosensor luminaire 1 2 3 workplane level Figure 1.1: Example intelligent lighting system configuration, showing the contributions of daylight and a neighboring luminaire to a photosensor. 2 photosensors on workplanes such as desks to measure this local illumina- tion. These could easily become obstructed in daily activities, for example through shadows cast by moved equipment or by the occupants themselves. The photosensors may be placed elsewhere, for example adjacent to the lu- minaires at the ceiling, containing the workplane in their field of view. See Fig. 1.1 for an illustration of this method of mounting. This way, the system becomes less obtrusive and practical. Note however that the distribution of light that has reached the workplane is not identical to what has reached the ceiling-mounted sensor. Hence, a translation will be required between the sensor reading and the illumination of interest at the workplane. 1.2.2 Lighting control algorithms Constrained optimization Two types of control algorithms may be distinguished for the intelligent lighting system. In a classical proportional integral differential (PID) ap- proach, only the illuminance measured with a photosensor is used [5]. In such case of standalone control the error with respect to a reference illumi- nance is computed and it is corrected for by the luminaire corresponding to that photosensor. This aims to achieve a decreasing error over consecutive control cycles. Alternatively, a constrained optimization problem may be solved to de- termine an optimal control action. In this case, the desired control behav- ior is expressed in a cost function which is to be minimized under a set of constraints. For example, the power consumption expressed in terms of the dimming level is minimized under the constraint that the minimum illuminance is achieved. The dimming level which minimizes the cost func- tion without violating the constraints then gives the optimal control action. This approach requires a mathematical model of the lighting behavior and its variables must be known in order to solve the optimization problem. Knowing only the illuminance sensed with a photosensor is insufficient. Es- timations will need to be provided for the variables used in the optimization problem formulation such as the component of daylight contribution at a photosensor. Networked control Most environments will require multiple luminaires to provide lighting to its entire surface area. In this case, the output light from one luminaire will contribute to the total illuminance in multiple photosensors' field of view, as illustrated in Fig. 1.1. Furthermore, based on occupancy conditions, the reference illuminance may differ across neighboring luminaires.
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