Sustainable Energy Storage for Furthering Renewable Energy
Bibliographic record
Abstract
John Harvey Kellogg noted that “food is simply sunlight in cold storage,” and stored food can prevent hunger when the field is hibernating. Likewise, excess energy can be put in “cold storage” for good use when needed. Specifically, energy storage is necessary for furthering intermittent renewable energy. This volume disseminates the latest progresses in sustainable energy storage for furthering renewable energy. The opening chapter, “Renewable Energy Storage: Too Many Options, Not Enough Time?” reveals that “too many cooks spoil the broth.” The most appropriate storage technologies are a function of the type, size, usage, etc. Also included is a chapter presenting the latest energy storage strategies along with projections of renewable energy sources including wind, solar, and geothermal. Another energy storage highlight is phase change materials (PCMs). If exploited appropriately, PCMs can save energy in buildings and electronic devices, including integrated chips for electronic systems and light-emitting diodes (LED) for revving vehicles, alike. A novel way of cooling photovoltaic panels to retain high energy conversion efficiency is to convert the heat into electricity via thermoelectric generators. A competing approach is to cool the panels with the help of PCMs and nanofluids. Thermodynamically, however, it makes sense to directly harness solar thermal energy for heating such as cooking whenever possible. Conventional solar cooking stoves suffer particularly from sun availability and intensity. A concentrating solar collector, thermal heat storage, and an efficient heat exchanger can bring this clean cooking technology into the future. The volume wraps up with a sentiment that, to further the progress toward sustainability, one cannot pretend we will drop fossil fuels instantaneously. <br>223 pages, © 2022
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".