A Simple Wireless Sensor Node System for Electricity Monitoring Applications: Design, Integration, and Testing with Different Piezoelectric Energy Harvesters
Bibliographic record
Abstract
Real time electricity monitoring is critical to enable intelligent and customized energy management for users in residential, educational, and commercial buildings. This paper presents the design, integration, and testing of a simple, self-contained, low-power, non-invasive system at low cost applicable for such purpose. The system is powered by piezoelectric energy harvesters (EHs) based on PZT and includes a microcontroller unit (MCU) and a central hub. Real-time information regarding the electricity consumption is measured and communicated by the system, which ultimately offers a dependable and promising solution as a wireless sensor node. The dynamic power management ensures the system to work with different types of PZT EHs at a wide range of input power. Thus, the system is robust against fluctuation of the current in the electricity grid and requires minimum adjustment if EH unit requires exchange or upgrade. Experimental results demonstrate that this unit is in a position to read and transmit 60 Hz alternating current (AC) sensor signals with a high accuracy no less than 91.4%. The system is able to achieve an operation duty cycle from <1 min up to 18 min when the current in an electric wire varies from 7.6 A to 30 A, depending on the characteristics of different EHs and intensity of current being monitored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".