The economical benefits of conducting an ASHRAE Level II energy audit
Bibliographic record
Abstract
Conducting an ASHRAE Level II Energy and Water audit provides building owners opportunities to save energy and water in their buildings. The ASHRAE Level II Energy Audit will fulfill the requirements for BOMA BESt Energy Assessment and IESO’s saveONenergy Electricity Survey and Analysis. The IESO saveONenergy allows building owners to receive monetary incentives to improve their energy efficiency. Energy audits are an effective method to increase energy efficiency for commercial buildings. However, there are multiple levels of energy audits set by ASHRAE (Level I, II, and III) which varies the level of detail and economic benefit. The role of this research is to explore the benefits of a Level II energy audit and the economic benefit of a office tower located in Toronto. This building had an ASHRAE Level I audit two years ago and a case study will be performed to evaluate the level of detail and economic benefit of a Level II Energy and Water audit. The tower was evaluated according to ASHRAE Level II guidelines and the results obtained were an Energy Star score for the building, benchmarking against BOMA BESt buildings, energy conservation measures (ECMs), financial savings, payback periods and CO2 savings. They were separated into low/no cost measures, capital measure, other measures and impractical measures. If the building managers were to target all of the recommended ECMs, a total of $300,000 in utility costs per year would be saved. This is equivalent to 1,700,000 ekWh saved per year and a 6% reduction of their current energy use. Further more, the total energy use intensity (EUI) would improve from 26.2 ekWh/ft2 to 24.7 ekWh/ft2. . This case study has allowed a comparison for the two different types of energy audit. Compared to a Level I energy audit, there is a lot more detail which can provide a better potential savings as there are more engineering calculations involved for mechanical equipment, reviewing of drawings, observation of mechanical equipment, and interviews with the building operators.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".