Pengaruh Morfologi Blok Perkotaan Terhadap Konsumsi Energi Bangunan Komersial, Studi Kasus: Kota Palu, Indonesia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".