Increasing the Capacity of Photovoltaics Using Proton Exchange Membrane Fuel Cell Backup within a Residential Community
Bibliographic record
Abstract
The popularity of the Feed-in Tariff program in Ontario has brought in a large number of renewable energy suppliers, especially in solar photovoltaic (PV) on the residential level.Due to the intermittent nature of PV energy generation, the capacity of PV is limited by the grid's ability to provide backup.This research explores the use of residential proton exchange membrane fuel cell (PEMFC) for backup purposes.Simulation was performed to assess the combined performance of PV and PEMFC systems in a grid-connected, hypothetical community located in Ottawa.A power management strategy was developed to operate the PEMFC system.The added PEMFC capacity in the community increased the PV capacity limit.This thesis also examined the feasibility of bringing the PEMFC technology into the Ontario market.Learning from stakeholder interviews, policy recommendations of a rebate program and a net-metering scheme were formulated to engage customers while increasing the province's generation capacity.v To my family, for their endless love and support.vi
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".