Introduction

The Fall 2025 iteration of Smart and Healthy Buildings culminates in eight student groups researching what the future of smart and healthy buildings should look like. The projects improve how we analyze data from buildings, show us how we can integrate AI better into future building-focused systems, and help resolve real-world problems with maintaining our buildings.

Time and Location

  • Date: Friday, December 12, 2025
  • Time: 2:00PM - 5:00PM
  • Location: Rice 340

Schedule

Time Paper
  Session 1: Indoor Conditions
2:05-2:15 Effects of Meeting Type on Indoor Environmental Conditions in Shared Spaces
2:17-2:27 Creating a Visual Interface to Display Indoor Conditions
2:30-2:40 Adaptive Noise Masking in Indoor Environments
2:40-3:00 Panel 1
  Break
  Session 2: Finding Failures
3:10-3:20 Follow the Schema: Finding the Valve Before the Flood
3:22-3:32 Physics-Informed Detection of Faulty Sensors in Smart Buildings
3:32-3:50 Panel 2
  Break
  Session 3: Do the Analysis
4:00-4:10 Use LLM to Analyze Sensor Data Stream
4:12-4:22 Spatial Energy Visualization and Nighttime Anomaly Detection for the University of Virginia’s Living Link Lab
4:25-4:35 AI Generated Building Summary: From Sensor Data to Daily Report
4:35-4:55 Panel 3

Papers

[code] [demo]

Authors: Seth Chase, Taran Gupta, Natalie Bretton, Abigail Corish,

In order to precisely characterize building power usage, sensors sample data multiple times per minute; however, this results in large, complex datasets that make meaningful insights hard to extract. An interactive, color-coded map of power usage provides an intuitive summary of how power usage varies both room by room and through time. This maintains data integrity while increasing usability. Data was first analyzed using Python, and then exported to Power BI to create visualizations that can be easily accessed. A room occupant can then use this output to see how their power usage compares to that of their neighbors or if a device has been left on overnight. It can be utilized by a building manager to find potential locations of energy latency, allowing them to make relevant adjustments, reducing power usage and costs.

AI Generated Building Summary: From Sensor Data to Daily Report

Authors: Samriddhi Kumar, Jack Hauger, Kushan Dixit, Evin St Clair, Benjamin Oppong

Our project develops an AI‑powered Smart Building Intelligence System that automatically processes real sensor data streams—such as temperature, CO₂, VOCs, occupancy, and environmental metrics—to generate daily operational summaries for facility managers. The system ingests raw building data, computes performance features, detects potential anomalies, and uses a large language model to produce clear, actionable reports. This enables faster decision‑making, enhances occupant comfort and safety, and modernizes building management workflows through automated intelligence.

Creating a Visual Interface to Display Indoor Conditions

Authors: Kayla Sprincis, Logan Brock, Xiling Meng, Danielle Sydow

Modern building sensors can provide valuable information regarding occupants’ health, comfort, and safety. However, the scale and complexity of collected time-series data often make the data’s real-world implications inaccessible to non-technical users. This paper presents a visual interface designed to translate real-time sensor measurements for six key building metrics – temperature, carbon dioxide, volatile organic compounds, humidity, noise, and illumination – into a digitally displayed, intuitive color-coded diagram. The system combines simple time-series analytics with a large language model (LLM), which interprets sensor readings, trends, and predefined health-related thresholds to select representative hexadecimal colors. A Streamlit web application displays the model’s outputs in a dynamic packed bubble chart, which adapts to emphasize the most critical metric levels at any given time. Tests for consistency demonstrate that the LLM’s color assignments remain stable for identical metric inputs and vary appropriately across distinct environmental conditions. The resulting interface streamlines interpretation of indoor conditions, rendering building data more accessible to technical and non-technical audiences alike. This work showcases the capacity of AI-assisted visualization to generalize IoT data, while highlighting future opportunities to improve accessibility, interpretability, and reliability through user studies and refined prompt engineering.

Use LLM to Analyze Sensor Data Stream

[code]

Authors: Wenhao Xu, Sonia Birate, Chenxu Li, Zeyang Zheng

Our project explores whether a Large Language Model (LLM) can interpret continuous sensor data streams and translate them into meaningful, human-readable insights. Modern buildings collect massive amounts of data from sensors like power, air quality, and lighting, yet these readings are often difficult to understand without expert analysis. We developed a Facility Manager ReAct Agent that retrieves real power-consumption data from InfluxDB, analyzes trends, computes statistics, and infers potential real-world activities occurring inside a room. By combining LLM reasoning with targeted tool calls, our system achieves 100% accuracy on numerical queries and reliably segments activity periods based on raw sensor patterns. This work demonstrates the feasibility of using LLMs as a semantic layer for building-health monitoring, reducing reliance on static rules and enabling smarter, context-aware environments.

Effects of Meeting Type on Indoor Environmental Conditions in Shared Spaces

[code]

Authors: Drake Ferri, Grace Fry, Matt Juntima, Joe Moran, Robel Woldegyorgis

Meetings are a major driver of occupancy in shared indoor spaces, yet little is known about how different meeting types contribute to short-term changes in indoor air quality (IAQ). This study analyzed environmental sensor data linked with 10,255 validated meetings held in the University of Virginia’s Living Link Lab from 2018–2025. A rule-based classifier assigned meetings to six categories: Admin/Leadership/Programs, Events/Outreach/Social, Instruction/Student Support, Research/Lab/Project, Walkup, and Other. Nonparametric statistical tests were used to evaluate differences in peak changes of CO₂ and volatile organic compounds (VOCs) relative to pre-meeting baselines. Results showed statistically significant but generally small differences in peak CO₂ increases across categories, with substantial overlap in distributions. VOC responses showed clearer differentiation. Instruction/Student Support and Events/Outreach/Social meetings tended to produce larger VOC peaks, whereas Walkup and Research/Lab/Project meetings showed smaller changes. These patterns indicate that meeting type provides meaningful structure for describing IAQ variability, particularly for VOCs, but explains only a portion of the overall variation. Overall, meeting type information may serve as a useful input for IAQ-aware building management, although practical control strategies will require integrating meeting classification with occupancy, ventilation conditions, and room characteristics. This study provides an empirical foundation for future work on IAQ-driven scheduling and ventilation planning in shared indoor environments.

Follow the Schema: Finding the Valve Before the Flood

Authors: Sriya Gandikota, Shaina Kumar, Nester Phiri, Navian Francis

Buildings are increasingly integrating cyber-physical systems within buildings. This paper examines the use of the Brick schema, a standardized metadata framework, to identify and localize valves during leak scenarios. The Brick schema is based on a mapping of valves, pipe segments, fixtures, and flow relationships across a building, independent of existing asset systems. The schema can be extended with domain-specific metadata and paired with a reasoning engine to provide actionable guidance for emergency response and maintenance workflows. The results from multiple test cases validate the schema’s alignment with Brick/UVA conventions and demonstrate its capacity to balance containment eFiciency with operational continuity. The model can identify optimal valves for leak isolation and quantify room-level service outages underscores its potential to enhance emergency response, maintenance planning, and overall building resilience. The system transforms static building information into actionable insights, laying the foundation for more scalable, intelligent infrastructure management tools.

Adaptive Noise Masking of Construction in Indoor Environments

[code]

Authors: Nyla Gordon-Crocker, Ian Straits, Greg Zeckman, Bhavya Boddu

Construction noise has become a persistent challenge for indoor work environments, particularly on busy campuses, where working amidst constant construction has become the norm. Investigation has shown that acoustic comfort is important to maintaining occupant health, concentration, and productivity. This study proposes a reactive noise processing model that utilizes machine learning to classify construction noise from recorded audio, thereby informing a conceptual white noise generator to mask and mitigate acoustic disturbances. A 24-hour waveform audio was collected from a personal office in Olsson Hall at the University of Virginia, with close proximity to the VERVE construction site. The audio was divided into 10-second chunks, with 1,619 manually labeled as either 1 (not containing construction) or 0 (containing construction). The manual data set was used to train a random forest classifier in an 80/20 train-test split, utilizing extracted acoustic features from Librosa, a Python-based audio processing software. The model correctly labeled 82% of audio chunks, proving the ability to identify construction noise amidst other sounds present in an academic space. Applying the classifier to the remaining 8,646 chunks revealed that construction activity clustered between 6:00 am and 11:00 am and 12:00 pm and 5:00 pm, consistent with standard workday patterns. To represent the conceptual actuation of a white noise generator, a tuned exponential moving average of model-based probabilities and hysteresis logic were implemented to generate stable ON/OFF control signals based on model probabilities. This study presents a framework that accurately processes ambient audio, identifying periods of high construction activity to activate a white noise masking system. The results highlight the potential to apply this system to real-time audio sources, thereby improving the acoustic health of occupants in developing environments.

Physics-Informed Detection of Faulty Sensors in Smart Buildings

Authors: Hunter Lutz, Shawn Thomas, Jessie Nothstine

This paper addresses the critical challenge of sensor calibration drift in smart building environments to help correct calibration errors which may undermine reliable building operation, leading to inefficient HVAC control, occupant discomfort, and erroneous performance analytics. We propose a physics-informed, automated framework to detect faulty sensors using only existing sensor data, without requiring additional hardware or manual calibration. We found that approximately one-third of sensors exhibited significant drift, with humidity sensors proving particularly vulnerable to miscalibration, and through field verification were able to draw correlation between drift and exposure to environmental microclimates. Combining statistical analysis with the discovery of potential physical constraints, we were able to reduce false positives and enhance detection reliability.