GSHP Project Feasibility and Sensitivity Analysis with RETScreen
Bibliographic record
Abstract
Ground Source Heat Pump (GSHP) is a highly efficient renewable and a clean energy source. The escalating interest in clean and renewable energy technologies is primarily to decrease dependence on limited primary fuels. GSHPs rely on the fact Earth surface temperature remains comparatively constant throughout the year, warmer than air in winter and cooler than air in summer. The feasibility of GSHP project is dependent on location and type of application. In present study the feasibility analysis of GSHP is done for different buildings at different locations using RETScreen 4. RETScreen 4 is free user friendly, excel based interface, developed by Canadian government (RETScreen | Natural Resources Canada) with build in climate data base, can be used in cost and time efficient wat to study the feasibility of Renewable Energy Technologies. Natural gas is selected as a base case for heating and electricity for cooling. Using RETScreen 4 it’s compared to proposed case in which load is shared or completely provided by GSHP. In conclusion, GSHP leads to considerable reduction in fuel consumption but its financial impact are quite sensitive to location, base case and nature of demand.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".