Finding the Best Station in Canada for Using Residential Scale Solar Heating: A Multicriteria Decision‐Making Analysis
Bibliographic record
Abstract
Solar energy‐based heating systems, which are capable of providing space heating as well as domestic hot water heating, are a promising alternative to conventional systems to achieve the status of reducing fossil energy consumption in residential buildings. Determining how suitable such systems are performing in Canada and which station is the most suitable in terms of energy‐economic‐environmental parameters are issues that have not been investigated so far. Considering that such results are very important for energy decision‐makers and investors, therefore, in the present work, the provision of space heating and hot water heating on a residential scale in 10 Canadian provinces was done by Valentin TSOL v2021 R3 software. Then nine software output parameters along with three parameters of land price, the population of each station, and the natural disaster index were weighted using the AHP method. Finally, the results of the stations were ranked using five MCDM methods including AHP, TOPSIS, WASPAS, CRITIC, and GRA. The results of numerical simulations showed that the CO2 emissions avoided parameter has the most weight, and the parameters solar contribution to DHW and boiler energy to DHW has the least weight. Also, the final ranking of each station showed that the most suitable station is Regina and the most unsuitable station is Victoria. By examining and analyzing the results, it was found that only based on the outputs of the Valentin TSOL v2021 R3 software, it is not possible to comment on finding appropriate and inappropriate stations, and the necessity of using ranking methods was observed more than before.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".