Abstract
Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO2. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.