Overview of Energy Savings and Efficiency Strategies at the University of Jordan Hospital
Bibliographic record
Abstract
The main objective of this study is to conduct an Energy Auditing of The University of Jordan Hospital. This hospital is quite known, as one of Jordan’s leading hospitals in human resources and equipment; however, the energy consumption of the hospital over the past few years has proven to be highly expensive. This makes the saving on costs of energy consumption a high priority that cannot be ignored. Energy audit covers the three main sections of the hospital: the Emergency Building, the Clinics Building and the Main Hospital Building. The consumption of energy, which costs the hospital a total of 2,778,426 JD/yr., can be categorized into three main parts, the lighting system, the diesel fuel consumption, and the consumption of water. After analyzing the energy invoices, and visiting the site for inspection, a list of recommended solutions that are applicable to cut down the total energy bill were derived. The implementation of renewable energy was suggested and studied of which a solar water heating system was proposed. A study on photovoltaic cells technology was performed for a sample of guest's rooms, the number of panels needed was found. After that, a feasibility study was conducted. The initial costs, annual savings and payback periods of the suggested system were estimated by the current market prices. It is found that the energy auditing and the solutions would be very beneficial as it will save a total annual value of 346,853 JDs (12.5% of the current energy bill).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".