Thermostats control heating and cooling in homes – representing a major part of domestic energy use – yet, poor ergonomics of these devices has thwarted efforts to reduce energy consumption. Theoretically, programmable thermostats can reduce energy by 5–15%, but in practice little to no savings compared to manual thermostats are found. Several studies have found that programmable thermostats are not installed properly, are generally misunderstood and have poor usability. After conducting a usability study of programmable thermostats, we reviewed several guidelines from ergonomics, general device usability, computer–human interfaces and building control sources. We analysed the characteristics of thermostats that enabled or hindered successfully completing tasks and in a timely manner. Subjects had higher success rates with thermostat displays with positive examples of guidelines, such as visibility of possible actions, consistency and standards, and feedback. We suggested other guidelines that seemed missing, such as navigation cues, clear hierarchy and simple decision paths. Practitioner Summary: Our evaluation of a usability test of five residential programmable thermostats led to the development of a comprehensive set of specific guidelines for thermostat design including visibility of possible actions, consistency, standards, simple decision paths and clear hierarchy. Improving the usability of thermostats may facilitate energy savings.

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.

Indoor air movement affects many functions of buildings, including ventilation and air quality, comfort and health of occupants, fire safety, and building energy use. Accurately measuring air movement has been difficult and expensive over extended periods of time, especially for velocities below 1 m/s. A new type of high frequency ultrasonic transceiver provides high sensitivity measurements and low cost through microelectromechanical systems (MEMS) manufacturing. However, at high frequencies, conventional ultrasonic signal processing algorithms function only over small ranges of ambient temperature and velocity. In this paper, we describe three algorithms that use the complex phase angle of an ultrasonic pulse to measure velocity and temperature over extended ranges of temperature and velocity. They employ heuristics to track the vibration cycle of the measured phase angle. These methods are applied in a pulse-based anemometer whose 176kHz MEMS transceivers both transmit and receive. In wind tunnel tests between 0-4 m/s, the tracking algorithm with a low-pass filter measured air speed with high sensitivity and accuracy (0.026 m/s mean absolute error). The ability to monitor to this accuracy with such low power draw and low cost is currently unprecedented in the industry.