Energy usage analysis of industries with ETAP case study
Bibliographic record
Abstract
There can be huge energy savings if industries are properly monitored and wastages are identified. After identifying the energy loss in Industries, corrective steps can be taken for saving Electrical Energy. To study Electrical Energy losses and to implement energy savings methods, a preliminary energy audit was carried out on a single industrial feeder in an industrial area at Chennai in Tamilnadu, India. The audit team selected 50 industries in that feeder and collected data for estimating electrical Energy losses by designing several questions. The data collection was done by visiting the industries and through telephonic conversation by the Energy Audit team. After analyzing the conditions that were prevailing in the Industries, three vital areas which were contributing Electrical Energy losses,(namely non provision of Capacitors for power factor improvement, non utilization of LED lamps and not using energy efficient ISI mark Star rated motors) were identified and study was conducted. The suggested implementation for the three areas was tested with ETAP simulation and substantial Electrical energy savings was noticed. The total payback period of the proposed recommendation is estimated as 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">½</sup> years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".