DATA-DRIVEN ENERGY MANAGEMENT: REVIEW OF PRACTICES IN CANADA, USA, AND AFRICA
Bibliographic record
Abstract
This research explores data-driven energy management practices in Canada, the USA, and Africa, offering a comparative analysis of successes, challenges, and future implications. Integrating smart grid technologies and supportive regulatory frameworks in North America has driven efficiency gains and sustainability. Conversely, Africa faces unique challenges, including infrastructural limitations, yet showcases localized successes in enhancing energy access. Lessons learned emphasize stakeholder collaboration and adaptable regulatory frameworks, providing valuable insights for global energy strategies. The study identifies gaps in technological infrastructure and recommends collaborative, context-specific solutions. As the global community moves towards sustainable energy futures, these findings contribute to a nuanced understanding, guiding policymakers, industry stakeholders, and researchers in shaping resilient and inclusive energy systems. Keywords: Data-Driven Energy Management, Comparative Analysis, Sustainability, Global Energy Landscape.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.044 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".