Occupancy-informed predictive control strategies for enhancing the energy flexibility of grid-interactive buildings
Bibliographic record
Abstract
• Analyzing and identifying the energy flexibility potential of occupancy records. • Integrating the occupancy prediction model into a control-oriented thermal model. • Designing occupancy-informed strategies for controlling building energy flexibility. • Evaluating occupancy-informed MPC in different scenarios and weather conditions. Building energy flexibility plays a major role in the stability of the current and future electric grid, especially with the rapid electrification in the building and transportation sectors that significantly changed electrical demand patterns. Accordingly, researchers have explored different venues to activate and control building energy flexibility without jeopardizing the occupants’ comfort. However, most predictive control strategies to date have only used occupancy information as soft or hard constraints, which limited the full potential of such information in enhancing the energy flexibility of buildings when incorporated into the decision-making process. To this end, this paper proposes a framework to develop and evaluate occupancy-informed control strategies to enhance buildings’ energy flexibility through occupancy variation. The objectives of the paper are to 1) analyze real occupancy data to extract typical patterns and the main occupancy levels, 2) develop a prediction model to forecast day-ahead occupancy profiles, 3) develop an occupancy-informed thermal dynamics model for controlling the indoor environment of a building, and 4) develop and evaluate occupancy-informed control strategies considering different scenarios and weather conditions. The developed framework was applied to an institutional building in Quebec, Canada as a case study and the results showed load curtailment of up to 40% and more than 60% energy cost reduction. These results established the important role of integrating occupancy schedules into control strategies which pave the way for integrating these strategies into the local energy market.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".