Appliance scheduling optimization with micro-grid in smart home network
Bibliographic record
Abstract
The fast emerging smart grid technology provides greater information flow, flexibility and control to both electricity consumers and electricity suppliers. Of these benefits, the two way flow of information between consumer and electricity producer in smart grid opened new vistas of applications. Smart home appliances are connected to home area network (HAN) to co-ordinate power usage demanded for the home under control. We are, therefore, witnessing an increasing interest in smart homes from the point of view of optimal energy management, renewable green energy sources and smart appliances. Hence, the problem of scheduling of smart appliances operations in a given time range with set of energy sources like national grid and local generation micro-grid is investigated in this thesis. Renewable energy source that is adopted in this thesis is a photovoltaic panel as a power producing appliance. Appliance operation is modeled in terms of un-interruptible sequence phases, given in load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm which can provide a schedule plan for smart home appliances usage is proposed based on the mixed integer linear programming technique. The effect of adding a photovoltaic system in the home results in reduction of electricity bill and the peak demand of the home and export of energy to the national grid in times when solar energy production is more than the demand of the home. The situation is modeled using Matlab with Yalmip library to exploit the state-of-the-art Gurobi solver for obtaining the timing of appliance scheduling in the smart home in comparable time to be true as real time process for demand side management.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".