Model-Based Predictive Control Strategies and Renewable Energy Integration for Energy Flexibility Enhancement in School Buildings
Bibliographic record
Abstract
This thesis investigates methods to enhance the energy flexibility potential of school buildings through simulation and experimental studies. It contributes a general methodology for the development of data driven grey-box thermal models and the implementation of model-based predictive control (MPC). The methodology is applied to an archetype fully electric school building near Montréal, Québec, Canada. This approach is scalable and transferable to other institutional or mid-size commercial buildings. \nTo streamline the implementation of MPC, the proposed approach employs grey-box low-order resistance-capacitance (RC) thermal network models, a clustering of weather conditions to identify typical anticipated scenarios, and several near-optimal setpoint profiles corresponding to each cluster. Archetype control-oriented models for zones with convective systems and zones with radiant floor systems are developed and calibrated with measured data. The calibrated models are used to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. For the experimental study, the developed MPC framework is applied in six classrooms, and the results are compared with four classrooms with the reactive control system as reference cases. The energy flexibility is quantified based on a proposed building energy flexibility index (BEFI). Results indicated that the school building can provide 45% to 95% energy flexibility (load shifting relative to reference) during on peak hours while satisfying thermal comfort constraints. \nFinally, this thesis presents an MPC methodology for the integration of air-based photovoltaic/thermal (PV/T) systems to further enhance the energy flexibility in school buildings so that in addition to the production of solar electricity, they can be used to preheat fresh air for the classrooms during the heating season. A data-driven grey box model for the classrooms is calibrated with measured data, and a PV/T model as a renewable energy retrofit measure for energy efficiency and flexibility is developed. These models are integrated to apply MPC and reduce peak demand during morning and evening. Results show that using an MPC along with PV/T integration can significantly reduce peak demand during morning and evening high demand periods for the grid. The proposed methodology helps institutional buildings to facilitate their integration into future smart grids and smart cities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".