Optimizing energy flexibility through electricity price-responsiveness and thermal load management in buildings with convective and radiant heating systems
Bibliographic record
Abstract
Building energy modeling is essential for designing energy-efficient and flexible buildings that seamlessly integrate with the electrical grid. This study introduces a data-driven, control-oriented methodology using Resistance-Capacitance thermal network models to accurately forecast building thermal loads. It differentiates the impacts of fast and slow dynamics associated with different heating types—radiant and/or convective. A Model Predictive Control (MPC) framework optimizes coordination between the different building thermal dynamics, considering weather forecasts and price signals. The Varennes Library, a Net Zero Energy Institutional Building located in Québec (Canada), serves as a case study for performance assessment. Validation of the developed model demonstrates its efficacy in enabling MPC to formulate effective control strategies. Findings reveal that high-mass radiant heating is strategically used before indoor setpoint variation or demand response events. Up to 70% of the building thermal load is delivered to the active envelope for off-peak heat storage and on-peak release. Conversely the ventilation heating is prioritized in proximity of the change in setpoint or grid tariff with percentages over 80%. Results show the adoption of weather clusters for generalizing the optimal control setting, highlighting their influence on thermal loads while maintaining robust ventilation and active envelope heating coordination. The comparison between the predictive control strategy and the existing rule-based control shows improvements in indoor temperature and energy flexibility. During the MPC routine, a constant price signal reduces grid stress, achieving Load Factor (LF) values up to 0.72 compared to 0.60 with rule-based control, while demand response, though critical peak pricing, optimally shifts up to 100% of the thermal load during peak price hours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".