A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System
Bibliographic record
Abstract
Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastrophic climate change. To address this challenge, we propose a model that allows for the exploration of a broader design space. We develop a supervised machine learning surrogate of a capacity expansion model, based on residual neural networks, that accurately approximates the model’s outputs while reducing the computation cost by five orders of magnitude. This increased efficiency enables the evaluation of the sensitivity of the outputs to the inputs, providing valuable insights into system development factors for the Canadian electricity system between 2030 and 2050. To facilitate the interpretation and communication of a large number of surrogate model results, we propose an easy-to-interpret method using an unsupervised machine learning technique. Our analysis identified key factors and quantified their relationships, showing that the carbon tax and wind energy capital cost are the most impactful factors on emissions in most provinces, and are 2 to 4 times more impactful than other factors on the development of wind and natural gas generations nationally. Our model generates insights that deepen our understanding of the most impactful decarbonization policy interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".