Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting.

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat’s setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant’s thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data’s sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method.