Joyce Kim

University of California, Berkeley | UCB · Department of Architecture

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occup...

The perception, physiology, behavior, and performance of building occupants are influenced by multi-domain exposures: the simultaneous presence of multiple environmental stimuli, i.e., visual, thermal, acoustic, and air quality. Despite being extensive, the literature on multi-domain exposures presents heterogeneous methodological approaches and in...

We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploi...

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important ob...

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant...

A personal comfort model is a new approach to thermal comfort modeling that predicts an individual's thermal comfort response, instead of the average response of a large population. It leverages the Internet of Things and machine learning to learn individuals' comfort requirements directly from the data collected in their everyday environment. Its...

Nearly 60% of global energy consumption in buildings is used for space heating and cooling to provide occupant comfort. Yet, a large portion of occupants are dissatisfied with the buildings’ thermal environment. There are many reasons for thermal dissatisfaction in buildings, but a fundamental cause is the current practice of delivering uniform the...

A personal comfort model is a new approach to thermal comfort modeling that predicts individuals’ thermal comfort responses, instead of the average response of a large population. However, securing consistent occupant feedback for model development is challenging as the current methods of data collection rely on individuals’ survey participation. W...

Personal Comfort Systems (PCS) are capable of maintaining occupant comfort in buildings despite large deviations from recommended "comfortable" temperatures. We present a novel digital controller for a well-studied (previously analog) PCS, allowing it to report real-time telemetry and respond to programmatic actuation requests. This enables the est...

Automated demand response (Auto-DR) is expected to close the loop between buildings and the grid by providing machine-to-machine communications to curtail loads without the need for human intervention. Hence, it can offer more reliable and repeatable demand response results to the grid than the manual approach and make demand response participation...

Open Automated Demand Response (OpenADR), an XML-based information exchange model, is used to facilitate continuous price-responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day-ahead hourly pricing. We summarize the existing demand response programs in New York and discus...

In New York State, the default electricity pricing for large customers is Mandatoryurly Pricing (MHP), which is charged based on zonal day-ahead market price for energy. With MHP, retail customers can adjust their building load to an economically optimal level according to hourly electricity prices. Yet, many customers seek alternative pricing opti...