Soft Sensing-Driven CO₂ Predictive Models in Educational Buildings

Indoor Air Quality (IAQ) plays a vital role in occupant well-being. Among various factors, CO2 concentration impacts the productivity and cognitive functions of occupants. Different strategies can be utilized to improve IAQ, including context-aware ventilation, air purification technologies, and integration of indoor plants. Existing methods in the literature for reducing CO2 concentrations rely on direct sensing, which requires advanced infrastructure that may prevent scalability. This study investigates a soft sensing approach, utilizing readily accessible features from Building Management System (BMS) to develop predictive models for CO2 concentration, offering a cost-effective alternative to direct sensor-based measurements. We leverage two different datasets to explore the feasibility and accuracy of the soft sensing approach. The first dataset aggregates CO2 data points compiled from existing literature, providing a broad perspective of IAQ variations across various educational settings. The second dataset is a publicly available, high-resolution set of IAQ measurements from several spaces over a month, allowing for detailed model training and testing. By applying machine learning techniques, we developed models that predict CO2 concentrations based on different sets of variables. We observed that the Random Forest model could predict CO2 concentration with a Mean Absolute Error (MAE) of 37.57 by utilizing room temperature, outdoor temperature, and the hour of the day. Moreover, this study assesses the transferability of the predictive models trained on a limited number of data points. We observed that using occupancy percentage results in more transferable models compared to other variable sets. The main contribution of this study to the body of knowledge is the evaluation of the soft sensing approach, which could pave the way for creating more scalable and infrastructure-independent systems to improve indoor air quality in educational facilities.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Application

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Meimand, Mostafa

Chairs dc:contributor.committeechair

• Ji, Bo
• Jazizadeh, Farrokh

Committee member dc:contributor.committeemember

• Cho, Jin-Hee

• Indoor air quality
• Soft sensing
• Educational buildings
• Predictive models
• CO2 Concentration

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/124331

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124331