Modeling and Performance Analysis for High-Rise Building Using ArchiCAD: Initiatives towards Energy-Efficient Building
Bibliographic record
Abstract
An energy-efficient building is not built in a day. It requires effective processes, approaches, and tools, as well as high commitment from all the involved parties. A similar requirement is needed for effective retrofitting practice. Building Information Modelling (BIM) is one of the sensible processes in ensuring either the new building development or retrofitting initiatives arrive at its ultimate objectives, i.e., reduction in energy consumption, energy cost, and removal of harmful emissions. Many studies had proved that a window is one of the building elements that could contribute to establishing an energy-efficient building. Therefore, a 25-floor Wisma R&D, University of Malaya building was modeled using ArchiCAD to analyse the influences of window glazing, opaque materials, and shading elements on overall building energy performances. The accuracy of the model and simulation outcome was initially compared with the energy audit result conducted from March to May 2017. Consequently, this study revealed that the effective combinations of the window parameters had assisted in improving the infiltration rate and heat transfer coefficient which allowed a lower cooling load within 3% to 6%, respectively. After most, minimum savings of 18,133.9 kWh, RM 6618.88, and 1265.16 kg of carbon dioxide (CO2) were gained through a reduction of cooling load in Wisma R&D based on the window system improvement. This article aims to promote the capability of ArchiCAD as a practical tool for effective retrofitting decision-making. Ultimately, this study revealed the importance of a multivariate framework in building energy conservation and provide an insight into the improvement of the Malaysia Standard MS1525:2019, mainly for high-rise buildings in Malaysia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".