Design and Development of a Self-contained and Non-Invasive Integrated System for Electricity Monitoring Applications
Bibliographic record
Abstract
Growing interest in improving the energy consumption efficiency in residential and commercial buildings has led to the emergence of intelligent energy management systems. This growing technology allows the transformation of the outdated electric distribution network within buildings to a smart and intelligent system. A major challenge in the development of such infrastructure is the need for low cost, integrated, self-contained, and non-invasive wireless sensor nodes. While an electric meter provides the utility company with information regarding the total energy consumption, no information is provided to the consumers regarding the energy consumed by individual appliances. Such visibility can provide consumers with the ability to better control and manage their energy usage leading to a reduced overall energy consumption. \nThis work explores the design and development of a self-contained and non-invasive integrated system intended for real-time electricity monitoring within residential and commercial buildings. The proposed system includes an Energy Harvester, an electric current sensor, a Micro-controller Unit, and a wireless communication device. The proposed system is self-powered and non-invasive, which offers a promising solution in providing real time information regarding the energy distribution within buildings. \nThe design featured in this work provides an innovative approach in the development of a customized interface circuitry that is designed to collect and regulate the energy from the Energy Harvester. The entire sensor node will operate under a power budget in the range of microwatts collected by the Energy Harvester. A Wireless MCU is programmed to acquire, process, and transmit the data from the sensor to the central hub via Bluetooth Low Energy connectivity. The real-time data transmitted to the central hub provides detailed information regarding the energy consumed by individual appliances within the building.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".