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Record W1565696922 · doi:10.5772/27428

Study of an Individual Air-Conditioning Energy-Saving Equipment

2012· book-chapter· en· W1565696922 on OpenAlexfundno aff
Wu-Chieh Wu, K-David Huang

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersNational University of SingaporeUniversity of Windsor
KeywordsAir conditioningConditioningEnergy (signal processing)Environmental scienceArchitectural engineeringEngineeringMechanical engineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.224
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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