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Record W2126661423 · doi:10.5539/jsd.v4n2p217

Optimizing Wind Power for Energy Efficient Building Design in Tropical Hot-humid Climate of Malaysia

2011· article· en· W2126661423 on OpenAlexvenueno aff
Abdul Malek Abdul Rahman, Hirda Lailani Khalid, Yusri Yusup

Bibliographic record

VenueJournal of Sustainable Development · 2011
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolRenewable energyWind powerEnvironmental scienceGlobal warmingElectricityIndigenousEnvironmental economicsRange (aeronautics)Air conditioningExtreme weatherClimate changeEnvironmental resource managementMeteorologyBusinessArchitectural engineeringGeographyEngineeringEcologyEconomics

Abstract

fetched live from OpenAlex

Being a member of the Kyoto Protocol, Malaysia is obligated to initiate programs to reduce global warming according to its own availability of indigenous resources. It is to explore and optimize whatever possibilities that can be contributed from its domestic region. Several initiatives have been taken to capitalize on what Malaysia is abundant with and one of its renewable resources is solar energy. It has been reported that to rely on wind energy would be futile because of the characteristics of wind in tropical climates are unpredictable, erratic and multi-directions. Several design strategies have been attempted to encourage air movement into Malaysian buildings but because of the nature of its climate where the average diurnal range is low, indoor air movement has to be induced by electric fan or air-conditioning. However, thus consumes a lot of electricity every month. This is clearly not good to reduce global warming. This paper explores of what extent can wind or air movement be contribute and be better utilized as an alternative energy to achieve Malaysia’s initiative to meet the Kyoto Protocol requirements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.223
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2011
Admission routes1
Has abstractyes

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