DETERMINANTS OF POWER OUTPUT PERFORMANCE IN MICRO HYDROPOWER PLANTS: AN ANALYSIS OF OPERATING AND INTERRUPTION TIME
Bibliographic record
Abstract
This study quantitatively investigated the relationship between the output power (kW) of the Bungin Micro Hydropower Plant (MHP) and two independent variables, operating time (hours) and interruption time (hours), using multiple linear regression. The analysis aimed to identify dominant factors influencing energy production efficiency. The resulting regression model, expressed as: Power (kW) = 3656.1 − 1.52 (Operating Time) − 1.02 (Interruption Time), indicates a negative correlation between both operating and interruption times and power output. However, statistical significance testing (p = 0.01) revealed that only interruption time had a significant impact on Bungin MHP's output power. This finding aligns with theoretical expectations that increased interruption durations impede energy generation. While operating time also exhibited a negative coefficient, its effect was not statistically significant (p > 0.05) within this model. Thus, interruption time is identified as the primary determinant of Bungin MHP's energy production, underscoring the critical need for effective interruption management to optimize plant performance.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".