Data-Driven Methodology for Model Order Reduction to Predict and Manage Building Energy Flexibility in Smart Grids
Bibliographic record
Abstract
The evolving energy landscape, driven by rising demand, electrification, and renewable energy integration, necessitates a shift from traditional “follow-the-load” model to demand-side management. This transition requires accurate prediction of building energy demand, effective demand response participation, and quantification of energy flexibility. This thesis develops a methodology for predicting and optimizing building thermal energy demand using data from smart thermostats and monitoring infrastructures. Multi-zone buildings and schedule-based operations are modelled using resistance-capacitance (RC) thermal networks. An automated model order reduction approach identifies dominant thermal zones in multi-zone buildings, while control-oriented RC archetypes capture key dynamics in schedule-based operations. Calibration follows a Model Predictive Control Relevant Identification (MRI) process, ensuring models accurately predict thermal dynamics up to 24 hours ahead. Weather variability is managed through clustering techniques that identify representative days, reducing computational complexity while enabling scenario-driven analysis. This approach bridges the gap between operational and design studies by integrating energy flexibility considerations early in building and community planning. A distributed economic Model Predictive Control (e-MPC) framework optimizes thermal load management while maintaining occupant comfort and system constraints. It supports applications at both single-building and community scales, such as virtual power plants. Performance is assessed using energy flexibility Key Performance Indicators (efKPIs) against a reference scenario. The methodology is validated through three case studies: (1) Residential buildings: 30 detached homes equipped with smart thermostats (data from Hydro-Québec); (2) Institutional building: The Varennes Net-Zero Energy Library, Canada’s first net-zero energy institutional building; (3) Community-scale system: A simulated hybrid photovoltaic-battery microgrid in Varennes serving residential and institutional buildings. Findings highlight how varying building participation in demand response influences aggregated demand profiles, utility metrics (load shifting, 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, and scalable design, this thesis provides actionable solutions for energy efficiency, flexibility, and resilience, supporting a sustainable energy transition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".