PhD Oral Exam - Anthony Maturo, Building Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

The evolving energy landscape, driven by rising demand, electrification, and renewable energy integration, necessitates a shift from traditional “follow-the-load” operations to demand-side management. This transition requires accurate prediction of building energy demand, effective participation in demand response, and quantification of potential energy demand flexibility.

This thesis presents a comprehensive methodology for predicting and optimizing building thermal energy demand using data from smart thermostats and other monitoring infrastructures. The building thermal dynamics are modelled through resistance-capacitance (RC) thermal networks for multi-zone buildings and buildings with schedule-based operation. An automated procedure, based on model order reduction through frequency-domain techniques, identifies dominant thermal zones and the related RC network structure in multi-zone buildings. Control-oriented RC archetypes are used to capture the key dynamics of buildings with schedule-based operations. Model calibration is performed using a multi-step process based on Model Predictive Control Relevant Identification (MRI) method. This routine ensures control-oriented models accurately predict thermal dynamics typically up to 24-hours ahead.

Weather variability is addressed through clustering techniques that identify a set of representative days. Internal validation metrics ensure the robustness of the selected clusters. This approach significantly reduces computational complexity of advanced controllers by reducing the simulation period to a set of representative days. Furthermore, it bridges the gap between advanced control studies and design studies by enabling scenario-driven analysis, facilitating the integration of energy flexibility considerations into both operational strategies and the early stages of buildings or community design.

Control strategies are implemented through a distributed economic Model Predictive Control (e-MPC) framework that optimizes buildings thermal load management while respecting occupant comfort and system constraints. This routine supports applications at both single-building and community scales, such as virtual power plants, and its performance, compared to a suited reference scenario, is evaluated using energy flexibility Key Performance Indicators (efKPIs).

The methodology is applied to three different case studies: (1) Residential buildings: 30 homes in Trois-Rivières equipped with smart thermostats (data provided by the public utility Hydro-Québec); (2) Institutional building: The Varennes Net-Zero Energy Library, Canada’s first institutional net-zero energy building, featuring advanced building energy management; (3) Community-scale system: A hybrid photovoltaic-battery microgrid serving residential and institutional buildings in Varennes, Canada.

Key findings reveal how varying levels of building participation in demand response influence aggregated demand profiles, utility metrics (i.e., load shifting and peak shaving), and the sizing of grid-supportive technologies. At the single-building level, insights are provided for optimizing thermal load management across convective, radiant, and mixed heating systems. By integrating data-driven modelling, advanced control strategies, and scalable design, the thesis offers actionable solutions for energy efficiency, flexibility, and resilience, supporting a sustainable energy transition.