Measurement and Verification of Energy Efficiency Savings in Industrial Facilities: The flaw of using energy intensities to determine savings
Bibliographic record
Abstract
Energy intensities have widely been used as a key performance indicator to track and report on the overall energy performance of an industrial plant or facility. The same energy intensity figures have then also been used to determine the energy savings over time. Energy managers have used reduction in energy intensities over time to determine energy savings. This is a method that has been widely applied across all types of industries and has worked very well over the last number of years, with the world economy booming and growing each year. However, with the slow-down in the economy, industrial plants suddenly started to experience drops in overall efficiency. All the savings they accrued over these years were “lost,” although no changes were made to the plants and facilities. The energy intensities of the plants started to increase. This article focuses on and describes the flaw of using energy intensities to determine energy efficiency savings. It describes how the physical characteristics of industrial plants are influenced by controllable and uncontrollable energy drivers and how these drivers should be incorporated into the measurement and verification process. The methodology is also applied to a case study.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".