Assessing Tradeoffs Between Solar Thermal and Wind Energy Integration in an Isolated Community Electrical-Thermal Grid
Bibliographic record
Abstract
Remote Northern communities in Canada suffer from unreliable access to energy.These largely indigenous communities derive their energy from fossil fuel-powered electricity generators and space heaters.This fuel must be transported long distances, which also contributes to nonrenewable energy consumption.Complicated travel logistics throughout the North further compound the issue.Renewable energy generators powered from sources such as wind and solar can largely address these issues.These generators can provide power on site, partly sidestepping the issue of fuel delivery, and greatly reduce greenhouse gas emissions.By employing a mix of thermal and electrical energy generation powered from wind and solar, a remote community's energy grid can serve the three chief residential energy loads: space heating loads, domestic hot water loads, and plug-in electrical loads.MoCreebec Eeyoud Istchee is a grid-connected community located on Moose Factory island in Northern Ontario.This thesis uses the case study of the MoCreebec community to determine the benefits of implementing a coupled electrical-thermal grid to serve the residential energy needs of 140 households.The electrical-thermal grid employs a solar thermal array with (and without) heat pump assistance, a wind farm, an electric heater, a district heating grid, and a thermal storage tank.A model of the proposed energy system is built and simulated in TRNSYS, using a combination of empirical data and estimation methods to determine the community energy loads.An analysis is conducted to investigate how the yearly household energy costs and greenhouse gas emissions of the current grid-connected community fare when compared to a community reliant Chapter 5: Discussion ........
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".