Decision-making method to prioritize and implement solar strategies on neighborhood level
Bibliographic record
Abstract
The current research presents a decision-making framework designed to facilitate the development and deployment of solar strategies in new and existing neighborhoods. Designing neighborhoods to achieve optimal solar exposure and integrate potential solar technologies involves numerous factors impacting the design process and decisions. These factors can relate to the neighborhood's layout as well as the proposed technologies and design strategies. Developers and other stakeholders often face the challenge of determining which strategies would be most beneficial for a specific neighborhood. The proposed decision-making tool evaluates solar design strategies to fulfill composite objectives, such as reducing total energy consumption, minimizing operational costs, achieving net-zero energy neighborhoods, and creating low/net-zero carbon neighborhoods. In addition, the user can also select specific objectives such as daylighting, passive heating, passive cooling, energy efficiency, electrical generation, thermal generation as well as combined electrical and thermal generations. The tool allows for the selection of suitable passive and active solar strategies based on the chosen objective. To assess these strategies, an adoption score-based decision-making criterion has been developed, which quantifies factors such as ease of implementation, feasibility (cost and accessibility), acceptance, and environmental impact. To establish the adoption scoring method, quantitative measures are determined through a survey conducted as part of the International Energy Agency (IEA) Task 63 on solar neighborhood planning. Experts with diverse backgrounds evaluated existing passive and active solar technologies and strategies. The application of this approach to specific neighborhood scenarios demonstrates its utility in assisting users in selecting the most appropriate solar strategies. This research contributes to the field by providing a comprehensive framework that integrates both active and passive solar strategies into urban planning. The decision-making tool supports stakeholders in making informed decisions by evaluating and comparing various solar strategies based on a multi-criteria assessment, thereby filling a critical gap in the existing literature.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".