Seasonal performance of a hybrid thermal energy system (geo-exchange and solar thermal) at Northern Alberta Institute of Technology (NAIT)-Part 1
Bibliographic record
Abstract
Standard energy systems for individual dwellings nowadays are still mostly conventional in North America and other places. Heat generation for a larger part is commonly met by using mostly natural gas while the use of renewable energy systems to replace fossil fuels is starting to become widespread, especially in Alberta, Canada, which is an oil driven region. It is of interest to evaluate the seasonal performance of a hybrid thermal energy system at NAIT, which is being used to meet the heating needs of the lab space in the D building and aid for teaching purposes. Seasonal performance of a hybrid thermal energy system (geo-exchange, solar thermal energy systems and air source heat pumps) at NAIT, will help analyse and describe the concepts and the parameters affecting the design and optimization of a heat source hybrid energy system, identify key parameters to help determine the optimal design of individual component as part of a larger system; highlight year-to-year performance of the system and overall design and optimization. The average yearly and total generation of the evacuated tube solar collector was reported to be 7.84 kWhs and 2268.59 kWhs, respectively compared to 1.59 kWhs and 987.15 kWhs for the geo-exchange system and 0.42 kWhs and 108.76 kWhs, respectively for the flat plate solar collector. The evacuated tube solar collector shows an overall improvement of 94.53%, 95.89% and 95.96% compared to the flat plate solar collector for the year 2015 to 2017, respectively. Comparing the geo-exchange system and the flat plate solar collector, it can be seen that the geo-exchange system outperform the flat plate solar collector with an overall improvement of 42.87%, 61.32% and 67.51, respectively.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".