Holistic Building Performance Evaluation: An Integrated Post-Occupancy Evaluation and Energy Modeling (POEEM) Framework
Bibliographic record
Abstract
A sustainable building performance requires the efficient use of resources while providing a comfortable and healthy environment for building occupants. While energy efficiency and environmental comfort metrics are commonly studied in the literature, they are mostly evaluated independently, potentially overlooking conflicting relationships that may exist between them in actual buildings. This paper presents a novel post-occupancy evaluation and energy modeling (POEEM) framework that overcomes the mentioned gap by combining the capabilities of post-occupancy evaluation (POE) and building energy modeling (BEM) to comprehensively assess the impact of energy conservation strategies on both buildings and their occupants. The framework consists of four main stages that include: (1) data collection on building design, performance, and feedback from occupants on the quality of their indoor environmental conditions, (2) building energy modeling and calibration to simulate current energy consumption levels, (3) statistical modeling of occupant-focused metrics such as comfort and perceived productivity, and (4) integrated evaluation of the previously-developed models to test strategies that minimize energy consumption without compromising occupants’ comfort and working conditions. In this paper, the framework is illustrated and validated through a case study of a green office building located in Abu Dhabi, UAE, where the authors assess the impact of alternative lighting intensities on building energy use, reported occupants’ comfort, happiness, and productivity levels. The results indicate that lighting energy levels can be reduced by up to 20% without compromising any of the studied occupancy metrics, confirming the potential of the proposed framework to identify occupant-centric strategies that improve building performance holistically.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".