Bibliographic record
Abstract
Subject: automated energy management systems. Purpose: identification of the main prospects for the use of automated energy management systems. Materials and methods : in Russia each year, due to the scornful attitude towards energy saving, about 400 million tons of reference fuel are lost, while only every third company approaches the energy efficiency systemically. Savings of fuel and energy resources largely depend on the proper organization and feasibility of rationing fuel and energy costs. One of the priorities of the strategy of transition to energy-efficient production is to carry out systematic work on the implementation of a system of resource and energy saving — the formation of the energy policy of the enterprise. The introduction and development of energy metering systems (AIIS TUE) for the sake of increasing the transparency of consumption and distribution of energy resources is an integral part of the implementation of the company’s energy policy. Minimizing the influence of the human factor at all stages is easily amenable to mathematical description and, accordingly, automation. The paper shows that without automation of management processes and monitoring energy efficiency it is impossible to count on success. The main stages of the implementation of the energy management system at the enterprise are given. Results : it is shown that the main result of the introduction of an automated energy management system is to reduce costs by more than 10 %, with the identified potential for further savings of at least 15...20 %, which is achieved by creating a completely transparent energy consumption environment, with its characteristic reliability determined efficiency of the processed information. Conclusions: automated energy management can become the core in which all information flows from all management systems that have an impact on energy efficiency will be consolidated. Properly constructed automated energy management systems are a universal tool for implementing the energy policy of an enterprise, a serious investment with a clear payback horizon and an positive financial result over a long period.
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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) | 0.001 | 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 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".