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Record W4412608584 · doi:10.22487/ruang.v18i1.159

Pengaruh Morfologi Blok Perkotaan Terhadap Konsumsi Energi Bangunan Komersial, Studi Kasus: Kota Palu, Indonesia

2024· article· en· W4412608584 on OpenAlexaff
Neyman Pearson Tanari, Nedyomukti Imam Syafii

Bibliographic record

VenueRUANG JURNAL ARSITEKTUR · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

The increasing global energy demand has become one of the issues in energy saving in buildings, not least in urban blocks where buildings and other buildings can influence each other. Urban block morphology with complex parameters can affect the energy performance of buildings at the block scale. However, on the other hand, analysis of the combined effect of urban block morphology parameters on building energy consumption is still lacking. In this study, the aim is to examine the methods and results of the effect of urban block morphology on building energy consumption. First, the research workflow for urban block energy assessment with geometry parameters as the basis for energy simulation. The sample urban block model formed is a building plot in the form of a hypothetical district of office buildings, which is then classified based on site coverage and building height. After that, the use of geometry parameters to evaluate and obtain the Energy Use Intensity (EUI) value for each sample urban block model is analyzed. Then, the combined effects of urban block morphology and geometry parameters on building energy consumption are evaluated and see how much impact they have in changing building energy use values. The results and analysis show that building density and height can directly affect building energy consumption. Increasing the values of site coverage and building height parameters has a positive influence on decreasing the EUI value.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.230
Teacher spread0.202 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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