Introductory Guide To The Relationship Between Electric Vehicles And The Electricity Grid & Identifying Barriers And Methods To Enabling A Transition To Electric Vehicle Infrastructure In Ontario
Bibliographic record
Abstract
The area of concentration of my Plan of Study is 'Land Use Planning and Sustainability.'This included the three components 'Land Use Planning,' 'Community Energy Planning,' and 'Transportation Planning.'Throughout my MES journey, I had difficulty selecting a focus area since there are so many interesting and important topics under the umbrella of sustainability.In the Fall of 2014, I met Christina Hoicka and was introduced to the field of Community Energy Planning.This was a 'eureka' moment for me.I discovered that there was a field of study that would cover a wide range of sustainability ideas, disciplines and technologies which when carefully identified could help communities improve their energy efficiency, reduce greenhouse gas emissions, and produce sustainable energy solutions.It was at this point that I felt that electric vehicles in particular, had the potential to make a positive impact towards sustainability.Additionally, electric vehicles became an increasingly interesting topic to me, because they overlapped with so many fields of interest such as planning, politics, building, electricity, storage, and renewable energy.Professor Hoicka made it clear that there is an emerging demand for people with the aptitude to communicate in interdisciplinary languages; particularly planners need to learn how to communicate with electrical engineers.This prompted me to teach myself the basics of electricity and electric vehicles.Through interviews and research I learned that interdisciplinary learning would not end here.Planning for electric vehicles required understanding the perspectives and politics of the Province of Ontario, local municipalities, the development Effectiveness in meeting stated aims:COMMENT: Changes would help meet EV goals of 1 in 20 vehicles be electric by 2020. Consistency with underlying Code objectives:COMMENT: This change aligns with the energy and water conservation and environmental integrity objective. Capacity of building sector to implement changes in a safe and effective manner:COMMENT: For an electrician wiring and installing a 240-volt rough-ins is no more difficult than installing roughins for a stove, or washer and dryer. Workload and liability implications for municipalities:COMMENT: No foreseeable liability concerns, but there will be an initial increase in workload due to a learning curve regarding EVs and EVSE infrastructure for municipalities regarding installation, operation, and maintenance.As well, there will be an increased cost in developing various documents including policies, departmental standards, guidelines, and possibly permits.There could be added work in educating internal staff and the development community.Lastly, this would be an additional element for building inspector review.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.028 |
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".