Solar design of buildings for architects: Review of solar design tools
Bibliographic record
Abstract
The International Energy Agency Solar Heating and Cooling Programme (IEA SHC) Task 41: Solar Energy and Architecture, gathered researchers and practicing architects from 14 countries in the three year project whose aim was to identify the obstacles architects are facing when incorporating solar design in their projects, to provide resources for overcoming these barriers and to help improving architects' communication with other stakeholders in the design of solar buildings.This report is a result of research done under Subtask B: Tools and methods for solar design, of the Task 41.The previous two stages of the Subtask B revealed that there is a broad variety of digital tools that architects are using today in their practices for solar design.The existing tools greatly differ in their complexity, the tasks that they perform, required input data and the output information.This possibly creates additional level of perplexity for those architects who need to choose appropriate tool in order to implement solar strategies at the early design phase, as the choice of tool incur cost, require time for mastering and affect the design workflow in the architectural practice.The purpose of this report is to provide guidance for architects through the variety of existing tools for solar design, both graphical and digital.Tools presented here were identified as the most used through the international survey of architects also done in IEA SHC Task 41.The intention is not to compare tools against each other, but rather to provide an overview of tools' capabilities to interested architects, in hope that it will help increase their overall awareness regarding tools and inspire them to use some of them when integrating solar strategies in their future designs.The second part of the report presents three exemplary case stories that describe different design approaches, tools that were used and how the use of solar design tools affected both design process and the final design solution.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".