Numerical study of residential Smart Dual Fuel Switching System (SDFSS) with on-site solar photovoltaic system
Bibliographic record
Abstract
Abstract Building energy consumption accounts for approximately 36% of total energy consumption in the world. Since buildings are capable of on-site electricity generation and exhibiting predictive pattern of heating and cooling, the focus of this study is to investigate the Smart Dual Fuel Switching System (SDFSS), comprising a natural gas furnace and an air-source heat pump (ASHP), working in conjunction with on-site solar photovoltaic system. Base-case scenario using 1.5-ton rated capacity ASHP with 8.5 heating seasonal performance factor (HSPF) yielded only 4% cost savings and 15% greenhouse gas (GHG) emission reduction when compared to natural gas furnace space heating scenario. With an ASHP of 2-ton rated capacity and HSPF of 10 working in conjunction with solar photovoltaic (PV) system of 4.08 kWp, the cost savings of SDFSS increased to 12% and the GHG emissions reduction of 45%. SDFSS integrated with the grid is expected to yield significant benefits. It can help avoid situation of buildings drawing electricity all at once and reduce the overload on the grid. Also, it can redirect electricity generated from the solar PV system of a building for which using natural gas furnace is more economical to a building whose space heating demands can be met with this excess electricity. Higher ASHP performance and capacity, higher carbon pricing, and higher solar PV electricity generation will lead to the design of a more economical and sustainable SDFSS.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".