Bibliographic record
Abstract
This thesis proposes an analytical method using a lithium-ion battery and generator data to provide insights and planning into a hybrid genset's optimal operational load, peak shaving, and fuel consumption difference with a standalone genset.The overview analysis is between the standalone genset and a battery-fused diesel generator.Further investigations into other factors play a significant role in the results, including power output (%load), battery depth of discharge, battery charge and discharge time (number of operations in a day), battery total cycle and life span of the battery.A numerical simulation was carried out to monitor the hybrid genset operation, whereby the battery depths of discharge and the number of cycles were compared.Another simulation is performed to find the incremental results for a 135 Kw genset by varying the different battery depths of discharge and the generator load difference.The interpretation of the result is that the depth of discharge decreases while the number of battery cycles increases, which will also result in a longer life span of the battery but is subjective to the daily operation time and load on the system.The combined results proved that the battery units will always generate adequate efficiency support for gensets. DedicationThis research thesis is dedicated to my parents, Hajiya Badiyya and Alhaji Muhammad Abba.And the people of Nigeria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".