Review and Characterization of Energy Harvesting Resources in Buildings with Case Study of a Commercial Building in Cold Climate – Toronto, Canada
Bibliographic record
Abstract
The present work provides a framework for the comprehensive assessment of energy harvesting resources in buildings, encompassing environmental, anthropogenic and recyclable sources. A review of resources and state-of-the-art energy harvesting technologies is presented including an outlook on the future theoretical limitations of their performance. The assessment framework is applied to a case-study commercial building located in Toronto, Ontario, Canada. Available resources are categorized into 3 orders of magnitude with respect to achievable power generation, with solar and wind in the first tier, elevator potential and fitness centres in the second tier, and sources including vibrations, occupant traffic and thermoelectric conversion in the third. Situated in a mid-rise context, the total annual resource magnitude is found to be 8 times greater than the building demand. However, only an overall 10% of the available resource is converted with the harvesting applications and efficiencies considered, resulting in a net energy deficit. It is shown that with maximum theoretical efficiencies, the conversion rate can reach 30% resulting in 151% surplus electrical generation for the building in question.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".