The impact of wildfire smoke on solar photovoltaic power generation in Alberta, Canada
Bibliographic record
Abstract
This study investigates the impact of wildfire smoke on solar photovoltaic (PV) power generation in Alberta, Canada, over a five-year period. As Alberta increasingly leverages its high solar potential to meet renewable energy demand, it is confronting the effects of climate change, particularly the escalating frequency and severity of wildfires and associated smoke emissions. Smoke has the potential to impede solar irradiation, thereby posing a significant challenge to solar energy production. Understanding the complex interaction between wildfire smoke and solar energy production is crucial for effective renewable energy planning and electricity grid management in the province. This study develops a Random Forest Regression model to forecast solar energy generation in Alberta. It incorporates PM2.5 measurements as indicators of wildfire smoke, alongside environmental parameters with strong predictive potential such as Global Horizontal Irradiance, and simulated solar energy generation data from current and future grid-connected solar sites in the province. The model is later used to isolate for the impact of smoke on solar power generation within the province. It is important to note that this study encompasses completed, proposed, and under-construction projects, as well as estimated financial data. Its aim is to assess the potential impact of smoke on Alberta's solar photovoltaic production potential, rather than provide a retrospective evaluation of historical events. Findings reveal an average 6.37% decrease in solar power production during periods of moderate to severe smoke, as compared to periods without smoke present, and that severe smoke levels are responsible for 76% of all losses. These results offer critical insights into the challenges and opportunities for renewable energy development in wildfire-prone regions and emphasize the need for proactive strategies to mitigate the impacts of extreme smoke concentrations on solar energy generation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".