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With buildings accounting for 70% of electrical demand, the electrical grid will require a more reliable and resilient demand side management from buildings. Building decarbonization is pushing for the advancement of grid-interactive efficient buildings (GEBs) to improve the energy efficiency and demand flexibility of residential households. In the last decade, smart home technologies have been explored to increase energy efficiency, improve occupant comfort, and leverage distributed energy resources (DERs) to increase the grid resilience of households. A smart and connected home testbed, consisting of identical 1,200 ft2 houses, has been designed and constructed to allow for an apples-to-apples comparison of smart home technologies. Potential research topics, which will be explored in this testbed, include the performance of high-efficient HVAC equipment, the energy and indoor air quality benefits with high-performance building envelope (e.g., wall, window, etc.), HVAC operation using smart sensors and controls, smart and connected appliances, etc. Furthermore, this testbed will enable the evaluations and comparisons of advanced demand side flexibility strategies with conventional controls in a controlled laboratory testing environment in terms of energy efficiency, load flexibility, and indoor air quality. The location of each house was determined considering surrounding environments (e.g., solar shadows). The testbed has been heavily instrumented with sensors and meters. For example, each house is equipped with a wide range of sensors, including thermocouples and heat flux sensors throughout to measure the heat gain of the building envelope. Indoor conditions will be monitored throughout the household with temperature, relative humidity, CO2, total volatile organic compound, and particulate matter sensors to measure the indoor air quality and the effects on occupancy comfort. A weather station set up on site will also report the local weather conditions for the houses. The buildings will be equipped with a humidifier and heater in each room to simulate the building loads from occupancy and other household appliances throughout the day. An hourly profile has been created for weekends and weekdays for each room to be followed. [ABSTRACT FROM AUTHOR]

Building energy consumption is strongly influenced by occupants and their energy use patterns. As such, there has been an increasing focus in recent years on the development and use of various sensing technologies to accurately infer occupancy information, which can be used to control building systems more efficiently such as lighting and HVAC. However, few studies have considered how to evaluate the ease of installation of such occupancy sensor systems, to support integration into a building's systems and controls. Thus, in this research, the concept of ease of commissioning (EOC) is proposed, which is a methodology to assess the ease at which an occupancy sensor system can (a) be brought into operation (including integration into a building's HVAC system) from out of the box to fully operational, and (b) continuously operate over time. The focus of this research is on the development of an EOC methodology and rating framework, based on a combination of a literature review, and analysis of both product reviews of off-the-shelf occupancy sensor systems to determine sensor feature preferences for installation and commissioning, and metrics used to evaluate other types of smart sensor systems. The developed method evaluates occupancy sensor systems based on several categories: (a) installation, (b) connection, (c) communication, and (d) power. Within each of these categories, several metrics are evaluated using a rubric-style evaluation method, which are then used to summarize both categorical ease-of-commissioning evaluation metrics, and an overall metric. As case studies, four occupancy sensor systems were evaluated using this EOC evaluation methodology. This study provides initial guidance for developing a universal ease of commissioning evaluation methodology of occupancy sensor systems, to help potential users better understand the relative challenges of installation and operational performance of implementing an occupancy sensor system. [ABSTRACT FROM AUTHOR]

Automated fault detection and diagnosis (AFDD) in residential heating, ventilation and air-conditioning (HVAC) systems has received lots of attention in recent times particularly with the advent of smart thermostat technology which has encouraged research into cost-efficient ways of performing AFDD without installation of additional sensors. However, investigation of some of the proposed smart thermostat-based methods using field data have revealed that smart thermostat-based data alone are not sufficient for a reliable AFDD. Most of these studies are based on simulation data rather than field data which have more disturbances. Moreover, the simplifications made in some of the studies further reveal the challenge which these AFDD approach would have if implemented in real life systems. Therefore, a more promising approach, i.e., an IoT-based approach that involves additional smart devices, is proposed in this study. The proposed approach makes use of a simple model, which was trained using outdoor dry-bulb temperature and indoor wet-bulb temperature. The trained model is then used to predict the enthalpy change across the evaporator of a residential HVAC system. The predicted enthalpy difference is next compared with actual enthalpy difference computed using temperature and relative humidity measurements from the return and supply side of the HVAC system and deviations between the two are used to detect presence of fault in the system. An experimental test was carried out in a test house located in Norman, Oklahoma. The results obtained show that the proposed AFDD algorithm was able to successfully detect low indoor airflow faults even with the use of measured data rather than simulation data as used in previous studies. Though unlike the thermostat-based approach, the proposed IoT-based approach requires one additional sensor, which often comes with smart thermostat purchase for additional room temperature measuerment. Hence with the placement of the smart sensor in one of the closest air difffusers, the studied AFDD can be done on a residential HVAC and cost savings can be realized for homeowners using our proposed IoT-based approach. [ABSTRACT FROM AUTHOR]