Open Source Data Logging and Data Visualization for an Isolated PV System
Bibliographic record
Abstract
Monitoring the operation of an isolated photovoltaic (PV) system needs both data loggers and web transfer to collect the sensor data. The data includes the measurement of the voltage and current of the PV system and for local weather. The PV system in Memorial University of Newfoundland (MUN) is 5 m away from the window, where the weather data is collected. In reality, PV systems are approximately 25–50 m away from the weather sensors. It is, therefore, more meaningful to realize the sensor communications by wireless transfer than long cables, which can significantly reduce the cables of a large PV system with long distances among sensors. The PC receives all the sensor data and transfers hem to a web server (Thingspeak). A web server is applied to monitor the operation of the system instead of a local server when its users are far away from the location, even though the local server allows more frequent data logging (once per second). The data transformation between the PC and the web server must guarantee the stability and robustness of the program. The system alarm that reports the disconnection failure is also necessary to notify the users. This paper will first introduce the general system setup, then present each part of the system in detail, and finally, analyze the collected data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".