Hohyun Lee

Recent developments in residential energy management necessitate fairness testing of energy technology and policy. Considering income level is particularly important because increased utility bills put low-income households at higher risk of economic and health issues. This paper proposes a novel method for generating diverse household energy models of varying income level and climate zone, which can be used to test the fairness impacts of residential energy developments. Models are stochastically generated using probability distributions based on data from national surveys. Included in the model are constant and time-variable features. Models capture the randomness inherent in the residential sector while still following realistic patterns in building structure, appliance stock, and occupant behavior. Validation of the model generation technique was done by comparing the energy consumption of simulated households to their counterparts in survey data. Models were validated with correlation analysis and by performing null hypothesis testing on the slope of a linear line of best fit. The null hypothesis that the trendline slope is equal to 1 could not be rejected (P > 0.05), and the data for all validation tests was very highly correlated (0.81 < r² < 1.00).

Occupancy information can save energy consumption buildings by 10-50 %. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control HVAC system. The installation process of occupancy sensors is often complicated and bothersome. Moreover, their high cost will not ensure short payback period, and thereby has limited the adoption of

Internet of Things (IoT) technologies (e.g., power-efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users’ privacy concerns. Low-cost and nonintrusive IoT sensors have been proposed for residential houses for use with machine-learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their nonintrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This article proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by nonintrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.

This paper presents a co-simulation platform which combines a building simulation tool with a cyber-physical systems (CPS) approach. Residential buildings have a great potential of energy reduction by controlling home equipment based on usage information. A CPS can eliminate unnecessary energy usage on a small, local scale by autonomously optimizing equipment activity, based on sensor measurements from the home. It can also allow peak shaving from the grid if a collection of homes are connected. However, lack of verification tools limits effective development of CPS products. The present work integrates EnergyPlus, which is a widely adopted building simulation tool, into an open-source development environment for CPS released by the National Institute of Standards and Technology (NIST). The NIST environment utilizes the IEEE high-level architecture (HLA) standard for data exchange and logical timing control to integrate a suite of simulators into a common platform. A simple CPS model, which controls local heating, ventilation, and cooling (HVAC) temperature set-point based on environmental conditions, was tested with the developed co-simulation platform. The proposed platform can be expanded to integrate various simulation tools and various home simulations, thereby allowing for cosimulation of more intricate building energy systems.

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO₂ sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.