Smart Home Appliances’ Scheduling by Two-Stage Optimization with Real-Time Price Model
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
Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances’ operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems.
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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 | 0.001 |
| 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