Energy Management Information Systems: Achieving Improved Energy Efficiency: A Handbook for Managers, Engineers and Operational Staff
Bibliographic record
Abstract
There are many opportunities for industrial and commercial facilities to improve energy efficiency by minimizing waste through process optimization. Large energy users can effectively reduce energy costs, improve profits and reduce greenhouse gas emissions by using computing and control equipment. This book covers all aspects of an Energy Management Information System (EMIS) including metering, data collection, data analysis, reporting and cost benefit analyses. EMIS provides relevant information to businesses that enables them to improve energy performance. EMIS deliverables include early detection of poor performance, support for decision making and effective energy reporting. EMIS also features data storage, calculation of effective targets for energy use and comparative energy consumption. Computer systems can be used to improve business performance in terms of finance, personnel, sales, resource planning, maintenance, process control, design and training. In the 1980s, the Canadian Industry Program for Energy Conservation (CIPEC) developed 2 versions of an energy accounting manual to help industrial, commercial and institutional sectors implement energy-accounting systems. The manual was revised in 1989 and is a useful energy management tool for business and other organizations. The EMIS examples described in this booklet reflect that energy is a variable operating cost, not a fixed overhead charge. 8 tabs., 38 figs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.091 |
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".