A critical review of field implementation of data-driven operation and maintenance technologies to reduce building energy use and GHG emissions
Bibliographic record
Abstract
Field implementation studies spanning six data-driven building operation and maintenance (DBOM) application domains (model-based predictive control (MPC), occupant-centric control (OCC), automated demand response (ADR), reinforcement learning control (RLC), fault detection and diagnostics (FDD), and virtual metering (VM)) were reviewed. The review aimed to make inter- and intra-application domain comparisons in terms of implementation processes and outcomes. The studies were classified by different levels of integration effort and sensing infrastructure complexity according to the quantity and kind of their inputs and outputs. An analysis is conducted on how energy-related success metrics (e.g., energy use, cost, demand) correlate with the integration effort and the complexity of the infrastructure. In general, MPC and ADR studies were found to require a higher level of integration effort than OCC studies; yet, OCC studies tend to require upgrades in the sensing infrastructure, unlike MPC and ADR studies. 95 % of those reporting post-implementation energy metrics achieved more than 10 % improvement in their energy-related objectives. The analysis results also highlight that a large fraction of the FDD and VM studies did not follow a measurement and verification procedure to quantify energy-related performance improvements upon field implementation. 90 % of the papers did not include feedback from building operators or occupants after the implementation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".