Economic Performance of Ground Source Heat Pump: Does It Pay Off?
Bibliographic record
Abstract
Electricity is usually supplied by diesel generators in remote communities at costs that can reach up to $1.50 per kWh in northern Canada.At these costs, several renewable energy sources (RESs) such as wind and photovoltaic (PV) can be cost effective to meet part of the energy needs.Their main drawback, being fluctuating and intermittent, can be compensated with either storage units, which are costly, and/or by adapting the electrical power consumption (load) to the availability of RESs.Electric water heaters (EWHs) are good candidates for demand side management (DMS) because of their relatively high power ratings and intrinsic thermal energy storage capabilities.The average power consumed by an EWH is strongly related to the set point temperature (Td) and to the hot water draw (Wd).A 5.5 kW, 50 gallon EWH is modeled in MATLAB-Simulink and a typical 24-hour water draw profile is used to estimate the potential range of power variation offered by an EWH for power balancing purposes.Besides, a strategy for controlling the power consumed by the EWH, by means of Td, using a grid frequency versus temperature/power droop characteristic is proposed.In this way, the EWH can be used for power balancing and for assisting with the mini-grid frequency control.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".