Exploring the Feasibility of Achieving Energy Self-sufficiency — A Residential Electricity Case Study in Ontario
Bibliographic record
Abstract
As energy security and climate issues are emerging as global concerns, it is commonly agreed that a transition from a conventional centralized energy system, which is largely based on combustion of fossil fuel, to a more sustainable decentralized energy system that includes mainly renewable energy sources is necessary and urgent. Due to the highly variable geographical qualities of renewable energy sources, spatial energy planning is becoming essential. This study aims to address the challenges in linking spatial modelling with assessment of regional energy consumption and renewable energy supply potential. \n \nA novel approach for exploring the feasibility of achieving energy self-sufficiency through matching energy deficit areas with energy surplus areas is proposed. A method for energy deficit and surplus area matching is developed and implemented in a VBA- based tool that serves as a decision-support tool by exploring possible future deployment of renewable energy in decentralized ways. \n \nAchieving Ontario residential electricity self-sufficiency through solar PV energy on an annual basis is explored as a case study. The results show that it is technically feasible for Ontario to be residential electricity self-sufficient through the development of solar PV energy with energy deficit areas within the region getting energy supply from nearby energy surplus areas. The case study implies that regional residential electricity self- sufficiency is achievable and it is useful for planners and policy makers to bear the regional energy deficit-surplus matching idea in mind when making urban and energy plans.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".