Cost and Load Reduction Using Heuristic Algorithms in Smart Grid
Bibliographic record
Abstract
Due to smart grid applications the consumers and producers are able to meet the demand of each others and thus take part in demand side management and demand response program. Hence smart grid leads to optimization of energy consumption and reduce high cost in today extensive demand of energy. In this research work we are reducing electricity consumption cost and load consumption using scheduling the appliances. The twenty appliances are used to schedule their energy consumption and load using heuristics techniques i.e. binary particle optimization, genetic algorithm and wind driven optimization, using the same data set for each technique and their results are compared with each other in order to find which technique do better optimization. Simulations are performed in matlab to show the cost and load reduction by the above three techniques and validate the experiment. The simulation results show that binary particle swarm optimization perform better than the other two techniques and wind driven optimization is better than genetic algorithm but not able to perform as binary particle swarm optimization, similarly genetic algorithm is least efficient as compared to both methods. Our research work is beneficial to meet the demand side management and help in reducing electricity cost and load for consumers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".