Tools & techniques for reduced energy consumption with residential energy system example application
Bibliographic record
Abstract
Finite supply and increasing demand characterize our modern energy landscape. Pressure from growing populations, increasing standards of living, and industrializing nations has continued to push energy demand upward. Our societies preferred energy sources are based on fossil fuels that have a finite supply. Although debate continues over the remaining levels of fossil fuel supply it is widely agreed that the sources that are easy to collect are reducing. With the easy to reach sources already in production, sources once thought to be not economically viable due to extreme environments and low-quality or diluted energy are being explored. In the case of the Athabasca oil sands in Alberta Canada, new processes have been developed to extract smaller amounts of oil from larger areas that would have been considered lost in the past.\nWhat is happening on the supply side in the Canadian oil sands is also happening on the demand side with cogeneration (using waste heat in power generation) and diurnal cold storage (capturing cold at night for use in day time space cooling). These are examples of getting useful work from previously discarded (cogeneration), unutilized (oil sands), and under-utilized (cold storage) energy sources.\nThis thesis focuses on the demand side of the energy equation in residential buildings. Specifically the paper focuses on conversion and use of energy in residential energy systems; space heating, space cooling, water heating, and refrigeration with the goal to reduce domestic energy consumption by sharing resources and combining components.\nThis research evaluates the feasibility of combining refrigeration and hot water production in a single heat pump system including a steady-state model of a residential vapor-compression refrigerator (heat pump) and energy and exergy analyses. The refrigerant cycle is modeled as steady-state while the cold and hot sink are dynamically modeled. Simulation duration is one day with a time step for dynamic calculations of ten seconds.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".