TOU-Aware Energy Management and Wireless Sensor Networks for Reducing Peak Load in Smart Grids
Why this work is in the frame
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Bibliographic record
Abstract
The electricity grid is undergoing a major renovation and becoming a smart grid by integrating the advances in Information and Communication Technologies (ICT). Current applications in energy generation, power distribution and its consumption need improvement in several ways, such as, making efficient use of green energy, increasing automation in distribution and enabling residential energy management. The existing grid does not provide sufficient mechanisms to manage the residential electricity consumption. However, interconnecting consumer devices with the home area networks, and at the same time, communicating with the utility networks through a home gateway facilitate residential energy management in smart grids. Residential energy management uses utility-driven price signals which vary depending on the time of the day. This is called as Time Of Use (TOU) pricing. In TOU pricing, electricity consumption during peak hours costs more than electricity consumption during off-peak hours. TOU prices reflect the variation in the actual cost of power during one day. Utilities run bas plants to supply power for the base load. In peak hours, demands of the consumers rise, and utilities bring peaker plants online to supply additional power. Peaker plants have higher operating costs and higher GreenHouse Gas (GHG) emission rates than base plants. Therefore, reducing peak load decreases the expenses for energy generation and it decreases the GHG emissions. Wireless sensor networks can play a key role in reducing the demand of the consumers in peak hours. In this paper, we employ TOU-aware energy management in a smart home with wireless sensor home area network and analyze the impact of this schemes on the peak load. We show that our scheme decreases the use of the appliances in peak hours and reduces the energy bills for consumers.
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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