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Low-cost energy conservation measures power saving impact on electronic appliances usage

2023· article· en· W4313654627 on OpenAlexaff
Ruchi Tyagi, Shaikh Shamser Ali, Suresh Vishwaakarma

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsEnergy conservationAir conditioningCarbon footprintComputer scienceEnvironmental scienceEnvironmental economicsEnergy (signal processing)Efficient energy useReliability engineeringAutomotive engineeringGreenhouse gasEngineeringStatisticsElectrical engineeringEconomicsMathematics

Abstract

fetched live from OpenAlex

Energy conservation measures use less energy to reduce costs and the environmental impact without compromising the consumer’s comfort level. Low-cost energy conservation measures play a critical role in power saving on the usage of electronic devices. The paper compares two different facilities using air-conditioning applications to study the impacts of low-cost energy conservation measures (LCECM) in real-time operations. The effect was recorded at both facilities by lowering the air-conditioning running time and increasing its set temperature ensuring that there was no compromise in occupants’ comfort level. As per international performance measurement and verification (IPMV) protocol A, data analysis was done using pre- and post-experiment readings. IPMVP output was analyzed more by running a t-Test in the SPSS software. Results indicated energy conservation with a cumulative impact on carbon footprint, environment, and cost of importing fossil fuel. The limitations of this study are that the energy conservation measurements were made with limited facilities and respondents' restrictions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0120.001

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.

Opus teacher head0.005
GPT teacher head0.222
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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