Framework for a User-centric Post-design Space Heating Energy Management System for Multi-family Residential Buildings in Cold Regions
Bibliographic record
Abstract
Building construction and operation are collectively responsible for over a third of the world’s energy consumption. Space heating is the highest energy consumer in the operation of residential buildings in cold regions; in order to reduce energy consumption within the building sector, energy saving measures for efficient space heating operation cannot be ignored. However, the current practice in multi-family residential facility’s space heating control systems is event-driven rather than user-centric and does not take into account the varying nature of occupant activity patterns. This research hypothesizes that integrating the uncertainties related to occupant load, along with weather disturbance and thermal performance of building envelope, in space heating management systems of multi-family residential facilities may contribute to increased energy efficiency. Hence, the present study develops a sensor-based user-centric post-design space heating control framework for multi-family residential facilities with a focus on efficient energy performance of the space heating system under occupancy. To demonstrate the proposed framework, a multi-family residential building in Fort McMurray, Alberta, is chosen as a case study. In this building, the existing space heating system is operated considering current outdoor climatic condition only. This research provides facility managers with a systematic, holistic framework to optimize multi-family residential facility’s space heating control system (e.g., producing heating energy by considering occupant demand, weather load, and the facility’s physical characteristics) in an endeavor to reduce the energy consumption from the building sector.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".