Design and implementation of a low cost web server using ESP32 for real-time photovoltaic system monitoring
Bibliographic record
Abstract
This paper presents a method for applying a web server based on ESP32 to a small photovoltaic (PV) power system to monitor and/or collect the PV and the battery current, voltage data. The designed system uses low-cost sensors, a microcontroller ESP32, Wi-Fi, and an SD-card reader. The ESP32 collects data from sensors. All of the data are saved in a text file on an SD-card, which is connected with ESP32 SPI pins. The text file is saved on the SD card for a week or longer, after which the system deletes all the data and starts saving new data. The web page file is saved on an SD card as well, so the ESP32 is programmed to access the web page by using the Wi-Fi via a laptop, cellphone, or tablet. Moreover, the web page has a link that allows users to download the data text file simply by clicking on the link. This can also be done remotely. The results of the experiments demonstrate that the web server works in real-time and can be effectively used for monitoring small solar energy systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".