Wireless Sensor Networks for smart grid applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electrical power grid is among the critical infrastructures of a nation. In the past several years, the power grids have experienced several major failures which have caused large financial losses in various countries around the globe. In a close future, the imbalance between the growing demand and the diminishing fossil fuels, aging equipments, and lack of communications are anticipated to negatively impact the operation of the power grids. For this reason, governments and utilities have recently started working on renovating the power grid to meet the power quality and power availability demands of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. The opportunities that have become available with the advances in Information and Communications Technology (ICT) have paved the way to this modernization. The new grid empowered by ICT is called as the smart grid. The natural extension of the smart grid applications to the consumer premises can be through Wireless Sensor Networks (WSNs) which are able to provide pervasive communications and control capabilities at low cost. WSNs have broad range of applications in the smart grid. In this paper we discuss the application of the WSNs in the home energy management services. We evaluate the performance of WSNs in terms of delivery ratio, delay and Packet Delay Variance (PDV) for varying interarrival times and varying network sizes. We also provide numerical results on the reduced cost, load and carbon emissions by our home energy management application.
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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) | 0.000 | 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 it