Emergency Energy Management Model for Durham Region
Bibliographic record
Abstract
In the last 50 years, the province of Ontario has lost electrical power at various times, which has challenged Ontario’s emergency-response capabilities. In addition to the loss of electricity supply, there were concerns regarding access to diesel fuels and gasoline due to loss of electrical power to pump the fuel. However, natural gas and propane are a viable alternative energy supply in an emergency scenario. The purpose of this project is to assess the role of natural gas in an emergency scenario and potential areas for further optimization to meet energy needs within the region of Durham, Ontario. An energy management model for the region of Durham has been developed for both electricity and natural gas. This model can be used to assess the impact of an emergency scenario on energy supply. This was achieved by researching different types of critical facilities such as hospitals, emergency services, schools, water/sewage facilities, community centers, shopping centers and gas stations and their reliance upon electricity and natural gas. Major shopping centers were included within this project as they provide communities with medical, grocery and basic needs. Data gathered for 415 facilities was incorporated into a Visual Basic model. The data was based on floor space, population, fleet size, water consumption, fuel types and energy use behavior. Facilities were divided into categories based on sizes of less than 5,000 m2, between 5,000 m2 and 15,000 m2, and greater than 15,000 m2 for the purposes of the model. Unique facilities such as the six water treatment facilities and the ten E.M.S stations were assessed individually. The data was then used to create a model that related the available electricity and natural gas to the various types of facilities in the Durham Region. The results show that natural gas infrastructure is already in place in the Region of Durham and many critical facilities currently use natural gas to supply heat energy. Hence, modest changes (cogeneration plants/ micro gas turbines) in the current infrastructure could be implemented to ensure emergency power is available from natural gas in a loss of electricity scenario and further improve the resiliency of the region of Durham in an emergency scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".