Energy and comfort performance benefits of early detection of building sensor and actuator faults
Bibliographic record
Abstract
This paper presents a building performance simulation-based investigation to better understand the energy and comfort performance benefits of early detection of common sensor and actuator faults. Five types of air-handling unit faults and four types of zone-level faults were implemented to the energy management system application of the building performance simulation tool EnergyPlus. During 50-year simulation periods, the faults were randomly permitted to affect 75 different components of an archetype medium-sized office building model. The sensitivity of the simulation results with respect to three variables was studied: fault recurrence period, fault repair period, and discomfort threshold for simulated complaints. The results indicate that the energy use intensity and the predicted percentage of dissatisfied exhibit a power–law relationship with time, in which most of the performance reductions occur in the first 10 years. If the work-orders are issued only upon occupant complaints, the faults were estimated to cause a 16–62% increase in the energy use intensity for heating, ventilation, and air-conditioning and a 11–38% increase in the predicted percentage of dissatisfied at the end of the 50-year simulation periods. The results indicate that if the faults can be detected within a month after their first appearance, almost all their detrimental effects on a building’s energy and comfort performance can be mitigated. Practical application: The methodology and results presented in this article are of practical use for those who study on-going commissioning, fault detection and diagnostics, and energy management systems in buildings. The simulation-based parametric analysis approach can be used to estimate the range of energy and comfort savings expected through early detection of common sensor and actuator faults in commercial buildings. Insights gathered from such an analysis can be used in planning the frequency of retro-commissioning and investments for automated fault detection and diagnostics systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".