Novel Predictive Home Appliance's Management Algorithm in Smart Grids
Bibliographic record
Abstract
Smart home appliances and applications are gaining popularity, due to the high level of service they provide to users. In a smart home, all electrical and smart appliances are interconnected together to form a special private network. Due to economic and environmental factors, energy consumption is of great concern to both users and service providers. The integration between the technological advancement in electricity grids and the environmental awareness led to the rise of smart grids. A reliable and well-grounded smart grid system can be achieved by well handling the in-home power requests. Thus, a key aspect in the design of smart grids is scheduling the start time and duration of run of electrical appliances to minimize the amount of energy consumed, as well as to force a cap on the maximum amount of energy consumed at any given time. In this paper, we present a scheduling framework for serving a request from electrical appliances in a smart home network. The network is assumed to allocate the available power to the incoming requests from appliances and serve each appliance at a fixed rate according to its initial requirements. Moreover, each request is assumed to have maximum bounds on both the tolerable start of service and the total interruption delays. The problem is formalized as a discrete scheduling problem which employs an adaptive algorithm. The proposed framework consists mainly of a scheduling mechanism formulated as a dynamic program. The paper presents two scheduling schemes: a non-preemptive and a preemptive one. We compare the performance of the proposed algorithm against other schemes from the literature. Simulation results show improvements in terms of consumed energy expressed as total saving in electricity bill cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".