Cloud-based Smart Dual Fuel Switching System (SDFSS) of Hybrid Residential HVAC System for Simultaneous Reduction of Energy Cost and Greenhouse Gas Emission
Bibliographic record
Abstract
A proper heating, ventilation, and air conditioning (HVAC) demand management system plays a prominent role in managing energy intake and taking a significant step towards sustainable housing with the goal of achieving net-zero emissions. Through experiments and simulations, this study investigated the viability of a recently developed adaptive cloud-based Smart Dual Fuel Switching Systems (SDFSSs) controller for residential hybrid HVAC systems. These systems consider various temporal parameters such as weather condition, building thermal demand, fuel price structure, and equipment capabilities into account to optimize space heating system operation, thereby lowering operating costs and greenhouse gas (GHG) emissions. Results show that by integrating SDFSS with existing ASHP technology, GHG emissions could be reduced by over 80 to 90% by 2030 with carbon pricing of $170/tonne. The results of this study could form a modelling and policy framework for different communities and regions. SDFSS would facilitate progressively and cost-effectively transitioning from existing fossil fuel-dominated heating systems to the low-carbon alternative of electric heat pumps powered by clean electricity. Due to the SDFSS’ responsiveness to the random and intermittent nature of renewable energy supply, hybrid heating systems equipped with SDFSS would facilitate wider penetration of renewable energy. Consequently, net-zero emissions could easily be achieved at a greatly accelerated pace before 2050.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".