Load-match-driven design improvement of solar PV systems and its impact on the grid with a case study
Bibliographic record
Abstract
The incorporation of solar energy systems into residential buildings is emerging as an important method of mitigating greenhouse gas emissions from the housing industry. However, several challenges accompany the deployment of solar PV for residential construction, such as determining an optimum size and layout design for best on-site system utilization in conformity to local roof sloping practices, especially in cold-climate regions. In addition, solar PV applications in high-latitude regions encounter other challenges, such as seasonal variations in daylight hours and in the sun's path, and soiling parameters such as snow coverage. These challenges result in a PV mismatch: (a) in winter, minimal PV-generated energy and high energy demand (due to space heating and hot water heating loads), and (b) in summer, PV over-generation and reduced energy demand. This paper aims to mitigate the impact of solar PV micro-generation and household loads on the utility grid by improving solar PV layout placement in order to maximize the system's load-match and minimize its grid interaction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".