Energy conservation for existing cooling and lighting loads

Abstract

Energy consumption by cooling and lighting loads can be significantly reduced if real time information on the occupancy is applied in their control. To meet this goal smart cooling and lighting devices, which have built-in sensors and controllers and which make decisions on individual basis, are being developed. However, it appears that no attempt has yet been taken for auto-operation of the huge number of already existent so called “non smart” air conditioning (AC) and lighting loads considering the count and location of occupants. A good deal of research has been reported in literature on the line of deploying or improving occupant detecting sensors/cameras, processing the captured images and developing high performance occupant identification (only detection or detection and counting) algorithms. Those were focused on locating users and surveillance in a building. A comparison of those works reveals that cameras combined with faster RCNN (Region based Convolutional Neural Network) algorithm can best identify the occupants. However, none of the works reported any prototyping considering occupancy with or without other ambient conditions such as comfort level, temperature, humidity and luminance for cooling load set point temperature update and/or lighting load control. Furthermore, the way the existing cooling and lighting loads can be made amenable to occupancy sensitive operation for energy saving in a noninvasive way is not yet reported. The novelties and contributions of the present paper are on bridging the gaps viz. (i) prototyping using traditional cooling and lighting loads of any brand already existent in a space for achieving occupancy sensitive energy saving, (ii) developing a model using the total count of occupants in a space along with ambient temperature and humidity for update of the set point temperature of cooling loads such that energy will be saved while the occupants will feel comfort, (iii) generation of ON/OFF commands in real time for lighting loads considering zone wise location of occupants along with the difference between ambient and required luminance in a zone, (iv) auto transmission of the updated set point temperature and on/off commands wirelessly from a common GUI (Graphical User Interface) respectively to the cooling and lighting loads without requiring any “hand held remote”, and (v) noninvasive implementation i.e. no need to modify or open the existing cooling and lighting loads at all and fixing any gadget inside these. The method proposed has been implemented on 3 traditional split type AC units of different brands and 16 composite units of LED (Light Emitting Diode) tube lights already existent in a lab. Compared with the manual mode of control which is insensitive to occupancy, the proposed method shows an energy saving in the range of 12.7%–36.15% for cooling loads while in the range of 35%–87.5% for lighting loads as the occupancy varies from high to low. The main economic and commercial impact of the present research is avoiding or postponing the replacement of the huge number of existing cooling and lighting devices by their smart counterparts which is often unaffordable for the entities in many countries.