Intelligent Building Automation for Net-Zero Energy Retrofit Projects

Thesis Abstract

Urban buildings account for a substantial portion of energy use and greenhouse gas emissions, yet retrofit projects often fail to achieve anticipated energy savings due to fragmented automation, suboptimal control strategies, and insufficient incorporation of occupant behavior. This study addresses the gap by examining how intelligent building automation (IBA) systems can optimize Net-Zero Energy Retrofit (NZER) projects through integrated sensing, data analytics, and adaptive control. The aim is to develop and validate an IBA framework that enhances energy performance, occupant comfort, and retrofit cost-effectiveness in existing commercial buildings. Specific objectives are (1) to characterize current NZER practices and identify bottlenecks in automation integration; (2) to design an IBA architecture that combines real-time monitoring, predictive analytics, and dynamic control of HVAC, lighting, and renewable energy systems; (3) to evaluate the framework’s performance against conventional retrofit approaches using empirical data; (4) to assess occupant comfort implications and acceptance of automated controls; and (5) to provide a decision-support toolkit for facility managers to optimize retrofit investments. A mixed-methods research design is employed. The quantitative component uses a quasi-experimental approach with two matched office buildings undergoing NZER one equipped with the proposed IBA framework (n=1) and a control building implementing standard NZER practices (n=1). Data collection spans 18 months pre- and post-retrofit, yielding a minimum of 12 months of post-implementation data for robust comparison. The quantitative data comprise energy consumption (HVAC, lighting, plug loads), indoor environmental quality (temperature, humidity, CO2), renewable generation, and occupancy schedules recorded at 5-minute intervals by building management systems and sensor networks. Statistical analyses include interrupted time series (ITS) to quantify energy savings trajectories, multiple regression to attribute variance to automation interventions, and ANOVA to compare comfort indicators between conditions. The qualitative component involves semi-structured interviews with facility managers (n=6) and end-users (n=40) to elicit perceptions of comfort, control, and usability, analyzed via thematic analysis guided by Braun and Clarke’s framework. The methodology integrates the Technological-Organizational-Environmental (TOE) framework and the Theory of Planned Behavior to guide instrument design and interpretation. The data collection instruments include calibrated environmental sensors, system logs, occupancy trackers, a validated occupant comfort survey, and a purpose-built automation audit checklist. Instrument validity and reliability are established through pilot testing, Cronbach’s alpha for survey scales (target ? ? 0. eight), and cross-sensor calibration. Data analyses will employ regression-based ITS models with autoregressive error structures to control for seasonality and external factors, complemented by system identification techniques to derive predictive models for energy consumption under varying occupancy and weather conditions. A regression-based model will be specified to isolate the marginal energy savings attributable to the IBA components, while a discrete choice model will examine occupant preferences for automation-driven adjustments. Key anticipated findings include (1) quantifiable reductions in site energy intensity (kWh/m2) and peak demand following IBA deployment, with expected average annual energy savings in the range of 18–28% relative to the control; (2) improved occupant comfort stability (maintained within ASHRAE comfort bands despite dynamic control) and higher user acceptance of automated adjustments compared with manual overrides; (3) insights into the relative contribution of predictive analytics versus real-time control to energy performance; and (4) a cost-benefit characterization indicating payback periods compatible with typical NZER project lifecycles when including avoided peak-demand charges and equipment downsizing. The study contributes to knowledge by operationalizing an integrative IBA framework tailored for NZER, bridging smart-building theory with practical retrofit constraints, and providing a replicable evaluation methodology for similar contexts. It offers a decision-support toolkit comprising a blueprint for sensor deployment, data governance, control strategies, and performance metrics. The main conclusion anticipated is that tightly integrated IBA can significantly advance NZER outcomes beyond traditional automation, provided that occupant engagement and robust data pipelines are established. Recommendations include scaling the framework to multi-building portfolios, continuous commissioning protocols, and policy guidance for incentives that reward automated energy optimization in retrofit programs.