Inclusive analysis and performance evaluation of solar domestic hot water system (a case study)
Bibliographic record
Abstract
In recent years Solar Domestic Hot Water systems have increased significantly their market share. In order to better understand the real-life performance of SDHW systems, a single detached house was selected for extensive monitoring. Two solar panels were installed on the house roof to provide thermal energy to the Domestic Hot Water (DHW) system. The house was equipped with data logging system and remotely monitored with performance data collected and analyzed over one year. The paper presents the inclusive analysis and performance evaluation of SDHW system, including DHW recirculation loop, under Canadian weather conditions for average family occupancy (two adults and two kids) with daily average DHW, draws of 246 L. Moreover, the study is carried out a significant recommendation to improve the SDHW performance, decrease the gas energy consumption and reduce greenhouse gas (GHG) emissions. The SDHW performance depends mainly on DHW flow rate, draw time and duration, city water temperature, DHW recirculation loop control strategy and system layout. The performance analysis results show that 91.5% of the collected solar energy is transferred to the DHW heating load through the solar tank. The contribution to DHW heating load is about 69.4% from natural gas and 30.6% from solar. The recirculation loop is responsible for close to 34.9%, of DHW total energy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".