Development of a Solar House Design Methodology and its Implementation into a Design Tool
Bibliographic record
Abstract
Buildings consume on the order of 40% of energy in Canada and the developed world. It has been demonstrated that buildings can supplement a large fraction (or all) of their energy use by collecting solar energy. In order to design such buildings, an integrated design process should be used, in which they are designed as a system rather than as discrete subsystems. Otherwise, opportunities for cost-savings are missed. Energy-conserving and energy-collecting upgrades should be considered early in the design process when costs can be minimized and disruptions to construction avoided. The optimal solution to solar buildings typically balances energy efficiency measures and energy generation, since they both have diminishing returns. \nHouses that offset their energy use with solar energy generation cannot justify the formality of the use of multiple designers because of the associated costs and potential cost savings. Therefore there is a need for a design methodology for solar houses and a corresponding design tool that can be used to support the process. It should enable the energy modeling of all relevant subsystems and provide guidance towards the near-optimal design space. The tool – called Ecos - will focus on early stage design and should enable the design of a near-optimal house within about an hour. \nThis thesis covers both a solar house design tool and the prerequisite work. There are four major interconnected parts of the work, including; a detailed energy model of a solar house; innovative ways of graphically representing performance data, a detailed design methodology, and finally the design tool itself. \nEcos provides two main types of graphical feedback: 1) visualization of the design space and 2) visualization of key performance metrics during solar design days. One of the methods to support efficient design is to provide quasi real-time feedback to the user. In order to provide real-time feedback to support an efficient design process, a combination of shortened simulation periods and regression models are used. \nThe final part of this thesis discusses recently built solar house and applies the current model in a re-design study to examine potential further reductions in energy use.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".