Results for “human-building interaction”

DOE OSTI2025

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗

DOE OSTI2023

Human-Building Collaboration: Toward lighting enabled collaborative system design

The profession of lighting design is evolving as contemporary lighting systems increasingly rely on cutting-edge computational technologies, sensors, and IOT systems. This trend requires designers to incorporate ideas of automation, dynamic controls, and user-system interaction into their design logic. However, real-time lighting simulations are constrained by inherent limitations arising from the reductive assumptions inevitably introduced in simulated lighting environments. This raises the question of how can designers account for the discrepancies between simulated and real lighting environments, and how can collaboration between humans and autonomous lighting systems bridge this gap. To address this question, we propose a protocol for designing collaborative interactions between humans and systems. Furthermore, this protocol builds on the Human-Lighting System Interaction Framework and demonstrates how human and system intelligence can be combined to fine-tune lighting qualities in a given space. Our paper shows how interactive lighting systems can customize lighting based on user preferences in real-time and how global lighting configurations can be adjusted over time. Specifically, we demonstrate: a) Human-system collaboration assumptions and goals, as well as how the protocol can be integrated into digitally programmable lighting systems. b) Implementations of collaboration that reveal how system autonomy, performance, and user experience are improved over short and long-term timeframes. c) How lighting design can be enhanced beyond simulation-driven design optimization capacities. The associated affordances and limitations are discussed with respect to existing lighting simulation design frameworks.

autonomous systems↗