The impact of smart grid residential energy management schemes on the carbon footprint of the household electricity consumption
Bibliographic record
Abstract
Smart grid provides remarkable opportunities for residential energy management. Residential energy management covers a large number of devices and techniques, from basic components, such as energy saving light bulbs to more complex methods, such as coordinating the household load. With the use of smart meters, smart grid enables two-way communication between the utilities and their consumers, where energy management becomes possible for both sides. Smart meters provide time-related consumption information which is used in Time Of Use (TOU) pricing. In TOU pricing, the price of electricity varies according to the time of consumption. For instance, the price of electricity is the highest during peak hours, i.e. when the load on the grid reaches its highest level. In peak hours, utilities bring peaker plants online which use more expensive resources such as coal, natural gas, etc. Besides, these resources have higher GreenHouse Gas (GHG) emissions. This implies that the time of consumption affects the carbon footprint of the consumers. Recently proposed energy management schemes rely on coordinating the appliances to avoid peak hour consumption and to make use of renewable energy sources. In this paper, we investigate the impact of these energy management schemes on the carbon footprint of an household due to electricity consumption. We show that energy management schemes decrease the peak hour usage of the appliances which consequently, decreases the carbon footprint of the consumers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".