The Effects and Financial Impacts of Wildfire Smoke on Solar Photovoltaic Power Production in Alberta, Canada
Bibliographic record
Abstract
As reliance on solar photovoltaic (PV) generation grows, particularly in Alberta, accounting for the impact of wildfire smoke on solar energy production is crucial. This is particularly relevant in regions with high PV generation potential, such as Alberta, as they are often more vulnerable to frequent and intense wildfires. This study quantifies PV energy losses and financial impacts due to wildfire smoke in Alberta, using fine particulate matter 2.5 (PM2.5) as a proxy for smoke pollution. Historical weather and PM2.5 data, along with simulated PV production from actual completed, proposed, and under-construction projects, are used to train and test the model. The simulated data is validated against real production data. The six-year study (2018–2023) covers major wildfire years and employs machine learning techniques, particularly random forest regression, to isolate the effects of PM2.5 on solar production. Financial losses are estimated in Canadian dollars, adjusted for inflation to December 2023.Results show a PV production decline of up to 6.3% at a single solar site over six years, with an overall average reduction of 3.91% under severe conditions. The cumulative impact led to a 0.19% average generation loss, equating to over $4.5 million in financial losses. Higher smoke levels consistently correlate with greater solar energy losses, aligning with findings from other regions. The results of this study enhance our understanding of climate change impacts on solar energy, highlighting wildfire smoke as a relevant factor. As PV adoption expands, these findings offer valuable insights for decision-makers and operational planners, emphasizing the need for strategies to mitigate smoke-related disruptions and ensure energy reliability.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".