Fast Demand Control in Smart Grid Communications with User-in-the-Loop (UIL) Method
Bibliographic record
Abstract
Recently, household energy consumption has been highly increasing, although there is a decrease in the source of energy.Consequently, the base price of electricity has been rising.Thus, there are a variety of approaches to save electricity costs.One proposed approach is the User-in-the Loop (UIL) method, in which the consumers (i.e.users) are given satisfactory incentives (for instance decreasing the base price of the electricity) to postpone their demand until low peak hours.To explore the effectiveness of this method, this study enrolls the open-loop control model and the closed-loop control model.While the open-loop control model is related to consumers' decisions of the process time relying on the low electricity cost, the closed-loop control model refers to consumer's reactions to the system's output and given incentives by electricity suppliers.To investigate the effectiveness of UIL, both control models are examined under three types of pricing models; namely, the fixed, current, and the dynamic pricing models.Whereas the base price of the electricity is steady in the fixed pricing model, under the current pricing model, it varies according to specified time periods in the current pricing model.Additionally, we propose the dynamic pricing model, in which the base price of the electricity is updated every 5 minutes based on a feedback message received from users.This feedback message is transmitted through ZegBee communication collecting the total amount of the electricity consumption of all home appliances.As a result of this study, we found that there is an inverse relationship between the number of users and the efficiency of UIL with the dynamic pricing model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".