Study of an Individual Air-Conditioning Energy-Saving Equipment
Bibliographic record
Abstract
Room air-conditioning does not always meet the optimal desire for thermal comfort of occupants and energy-saving goals This is due to the following reasons: It is well established that differences exist between individuals in required comfortable temperature for their own local environments. (ii) Traditional air conditioning airflow distribution can stagnate around furniture, lights, and partitions which mean that airflow is distributed without consideration for the occupants' comfort demands. (iii) air-conditioning airflow has been distributed without consideration of occupants' demands beyond setting temperature and fan speed to meet thermal comfort standards, such as Predicted Mean Votes (PMV) and Predicted Percentage of Dissatisfied (PPD) indices (ISO-7730 2005) [2]. It may be that the actual presence of occupants in the workplace is less than that was set up from the original air conditioning design. This causes serious waste of cooling load to the system through unnecessary runtime. (iv) The traditional air conditioning air supply unit is set high on the ceiling or wall, which means that much of the cool air proportionally much consumed by light fittings, walls, and other surrounding objects. Therefore, this increases the demand on energy supply. In response to these concerns, several personalized airconditioning systems have been showed and it rightly remains a great attention in efforts to design more intelligent workplaces. Cho and Kim [3] introduced a personal environment module (PEM), the PEM was studied as an alternative air-conditioning system for improving thermal comfort in workspaces. Compared to a typical under-floor air-conditioning system, the advantages of the PEM is more flexible and easier to control. Zeng et al. [4] introduced personalized ventilation systems (PVSs), that allow each occupant to control their own thermal comfort provision, such as airflow direction and temperature. The perceived air quality of the PVS is superior to that of a conventional ventilation system with the same amount of supplied air. Melikov [5] have systematic reviews on the performance of personalized ventilation (PV) and on human response to it. PV, in comparison with traditional ventilation, has two important advantages: (i) it has potential for improving the inhaled air quality, and (ii) each occupant is delegated the authority to optimise as well as control the temperature, the flow www.intechopen.com
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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".