Remote Data Acquisition System for Photovoltaic Water Pumping System in Sukkar, Pakistan
Bibliographic record
Abstract
Access to high-speed internet connectivity is limited in Sukkur, Pakistan, making alternative communication technologies essential for real-time monitoring and control of photovoltaic (PV) water pumping systems. This research paper presents the design, implementation, and evaluation of a GSM-based remote data acquisition and logging system for a PV water pumping system in Sukkur. Leveraging abundant sunlight in the region, the proposed system utilizes 2G GSM technology for communication between the PV system and the remote monitoring station. A network of sensors captures key parameters, and the acquired data is processed, stored, and transmitted using 2G GSM, enabling remote access and real-time monitoring from any location with GSM coverage. The implemented system incorporates an Arduino microcontroller for core operation and employs an SD card for data logging. Real-time data logging allows for detailed tracking and analysis of system performance, facilitating troubleshooting and optimization. Data stored on the SD card can be transferred to a computer for further analysis using data analysis software or custom applications, providing meaningful representation of trends and insights into system operation. The system also features an OLED display for real-time feedback on essential parameters, including solar irradiance, water level, and pump status. Furthermore, the integration of user prompts and GSM communication enables remote monitoring and control, empowering users to inquire about system status and remotely activate or deactivate the pump through SMS commands. The system offers a robust and adaptable solution for efficient management and maintenance of the solar-powered water pumping system in Sukkur, Pakistan.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".