Optimizing photovoltaic systems to decarbonize residential arctic buildings considering real consumption data and temporal mismatch
Bibliographic record
Abstract
Nunavik is a remote region in northern Quebec, Canada relying on off-grid diesel-based electricity production. In this study, photovoltaic (PV) systems for residential buildings are optimized using real electricity consumption data. The region is characterized by a significant temporal mismatch between electricity demand and PV production. Two PV systems are studied: standalone arrays and building-integrated systems (BIPV). Three multiobjective optimization problems are formulated to represent different ways to manage the demand-production mismatch, involving objectives such as the mean squared error between production and consumption, penetration of solar energy, energy gap between production and usage, and PV size. Solutions were obtained using a genetic algorithm (NSGA-II). It was found that moderately sized PV systems (60–140 m2) could cover about one third of the instantaneous electricity demand of a semi-detached house, yielding an average annual GHG emissions reduction of 1.112 ton CO2 per house. An important surplus was found with two optimization problems, suggesting a potential for reinjection in the microgrid or batteries. Optimal designs of PV systems for both configurations were influenced by how the mismatch is managed, i.e. the choice of objective functions. The first optimization problem minimized excess energy by favoring less sunny directions, while the second and third supported energy storage or surplus reinjection, favoring south facing or vertical PVs. A robustness analysis underscored the importance of matching PV system design to consumption profiles. Ultimately, this study contributes to the emerging field of renewable energy integration in the Arctic, aiming to reduce reliance on fossil fuels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".