Monitoring of a 1 kW[sub p] Solar Photovoltaic System
Bibliographic record
Abstract
A 1 kWp ‘stand alone’ PV system consists of 4 module (2 BP SX75U module and 2 BP 275F module), inverter, 2 thermocouple, 3 voltage sensor, 3 current sensor, 4 battery and data logger (Data Taker DT80) has been set up. This research involve nine parameters which are irradiance (Ia), ambient temperature (Tamb), module temperature (Tmod), module voltage (Vmod), battery voltage (Vbat), load voltage (Vload), module current (Imod), battery current (Ibat) and load current (Iload). All parameters were measured using the equipments and sensors that connected directly to data logger (Data Taker DT80) to interpret and show the data on computer using the Delogger sofware. The data then was transferred into the computer and analyzed using the Deview and Microsoft Excel software to determine the performance indices for the stand alone PV system. From the analysis a few performance indices were determined. The range of daily solar irradiation is between 2.20 kWhm−2 to 4.00 kWhm−2, while the range of total global irradiation is between 5.76 kWh to 10.48 kWh. For daily total energy yield, the range is between 0.23 kWh d−1 to 0.28 kWh d−1. The range for clearness index is between 0.49% to 0.89%. The range for final yield is between 0.77 kWh d−1 kWp−1 to 0.93 kWh d−1 kWp−1 while the range of array efficiency is between 2.53% to 4.65%. Lastly, the range of the performance ratio is between 22.08% to 40.58%.
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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) | 0.001 | 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 | 0.001 | 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".