Energy Efficiency in a New University Campus: Preliminary Findings
Bibliographic record
Abstract
One of Malaysia’s national energy policy’s objectives is to promote efficient utilization of energy and the elimination of wasteful and non-productive patterns of energy consumption. The government wishes to intensify energy efficiency (EE) initiatives in a broad range of areas, including in government buildings. University Malaysia Perlis (UniMAP), a Malaysian public institution of higher learning, has been chosen for an action research in the implementation of an energy efficiency program, in line with the government’s aspiration. This highly populated organization was an ideal selection for an energy consumption and silent waste research. Research objective was to identify areas of possible energy waste within the Campus. To achieve the objective, 2 projects were selected: one was to reduce energy use of chillers and the other was to find if there is any wastage with wrong setting of luminous flux in specific areas. For the first project, the staging method was adopted, where chillers will be loaded with 15 minutes intervals and for the second, luminous flux was set according to government body’s requirement to attain energy saving as initial stage findings showed that luminous flux setting was more than what is required. The results from just these 2 projects demonstrated, that UniMAP was able to save approximately 53,000 kWh of electricity within the research duration. From the preliminary results, it is apparent that more energy wastage analysis within the campus should be carried out in order to maximize potential savings that can be achieved in the future.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".