Heuristic Algorithm Based Energy Management System in Smart Grid
Bibliographic record
Abstract
Smart grid is one of the most advanced technologies which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. Home energy management (HEM) benefit the end user by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM as it manages energy demand according to supply by automatically controlling the appliances or by shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are used to meet these objectives. In this research work, we evaluate the performance of HEM which is designed on the basis of heuristic algorithms, wind driven optimization (WDO), ganetic algorithm (GA) and binary particle swarm optimisation (BPSO). Finally, simulations are conducted in MATLAB to validate the performance of scheduling techniques in terms of cost, reduced peak to average ratio (PAR) and equally distributed energy consumption pattern. The simulation results prove that WDO algorithm based HEM proves to perform efficiently than BPSO and GA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".