Development, Implementation, and Industry Reception of a Novel Multi-source, Data-driven Building Energy Management Toolkit
Bibliographic record
Abstract
Building energy performance is often negatively impacted by inefficient and uninformed operations, leading to wide disparities between predicted and actual energy use in commercial and institutional buildings.Though ample research in data-driven building operations and maintenance analytics has derived various methodologies for extracting energy-saving insights that can supplement operating practice, these approaches have traditionally remained disparate, limiting their application, and are exclusively prevalent in academia.Furthermore, operations personnel who regularly manage controls and maintenance of HVAC equipment can benefit from these novel approaches in augmenting their duties and optimizing building energy efficiency.This research explores the development of a novel multi-source, data-driven building energy management toolkit as a synthesis of established data-driven approaches in the literature comprising inverse energy modelling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods.The toolkit inputs various data types to detect hard and soft faults, optimize sequences of operation settings, and monitor energy flows, occupancy patterns, and occupant satisfaction.The toolkit's unique multi-source analytical approach was used to pinpoint operational deficiencies stemming from inappropriate zone temperature overheating thresholds and perimeter heating devices.Energy-saving insights were generated using data from four separate case study buildings to demonstrate the utility of the toolkit's web-based application platform.Finally, interviews with building operators and facility managers to their interpretations of insights from data-driven approaches were conducted; possible barriers were identified which inhibited industry professionals from effectively deriving and utilizing insights from the visualizations and KPIs.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".