Design of a photovoltaic system for a house in Pakistan and its open source ultra-low power data logger
Bibliographic record
Abstract
This thesis presents an open-source, ultra-low powered data-logger for off-grid photovoltaic (PV) applications. An off-grid PV energy system is also designed for a rural house in Pakistan. The real-life power consumption data of this house is collected for the design and simulation purpose. The expected annual output energy of designed system is calculated by using Homer Pro software. Annual solar irradiance, average temperature and other environmental aspects are also considered for simulation of the designed system in Homer Pro. The data-logger is designed to log major parameters of designed PV energy system. Deep-sleep mode of ESP32-S2 microcontroller is used along with voltage, current, and light sensors for logging the data of PV system in an external micro SD card. Data-logger is programmed to operate in deep-sleep and web-portal monitoring modes and a manual or automatic switch is used to select these modes. Real-time PV data can be monitored in a local web-portal programmed in the microcontroller only by switching the toggle switch to on position. The same web-portal is also used to check and download the historical data of a PV system. The energy consumption of the designed system is 7.33mWh during deep-sleep mode and 425mWh during the web-portal monitoring mode. The total cost of the designed data-logger is approximately 30 CAD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".