Post-occupancy evaluation of energy and indoor environment quality in green buildings: a review
Bibliographic record
Abstract
The need to reduce energy use as part of a strategy to alleviate environmental stresses is widely accepted. Buildings are big end-users of energy; buildings account for 20-40% of the energy demands in developed nations, and the rate of new building construction in developing nations is accelerating. To reduce the impact that buildings have on the environment, the need for them to use as little energy as possible while still providing a satisfactory indoor environment is critical. The green building movement may be an effective catalyst for this, and various green building rating schemes are now in the marketplace worldwide. Certified 'green' commercial buildings exhibit higher real-estate values, presumably reflecting expectations for reduced operating costs, and improved organizational productivity through better indoor environments for employees. However, the higher market value cannot be maintained in the long run if these buildings do not deliver their expected benefits. The early generations of 'green' certified commercial buildings have now been occupied for several years, and it is time to explore whether these 'green' buildings are living up to expectations in objective terms. This paper reviews several of the post occupancy evaluations (POEs) that have been performed. A limited number of POEs are available in the public domain, making it difficult to draw solid conclusions. However, early trends suggest that green buildings on average seem to be delivering reduced energy use, however a large spread in performance is often observed meaning that individual buildings do not always perform as expected. Occupant satisfaction with some aspects of the indoor environment appears to have improved compared to conventional buildings, but there are areas where expected improvement trends are not realized. This paper provides some possible explanations for the observed performance, and describes a new, Canadian-led, research project that aims to explore these issues further.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".