Efficient Energy Management for Household: Optimization-Based Integration of Distributed Energy Resources in Smart Grid
Bibliographic record
Abstract
Energy demand is increasing globally due to the growing human population and progressive lifestyle. The adequate use of available energy resources, including renewable, contributes to a country’s economic sustainability and future development. Optimization–based energy management and cost minimization plays a significant role in overcoming energy crises in less developed countries. In this paper, an optimization–based dynamic energy management technique for smart grids is developed based on the integration of available renewable resources and variable consumer demand, distinctive to underdeveloped countries. Consumer demand is classified into fixed, flexible, and highly variable based on population characteristics. In this work, we developed a Dynamic Multiple Knapsack DMKNS algorithm, which automatically schedules energy provision to various users by optimally accounting for the available resources (Grid and Renewable). The proposed method provides a low-cost solution by maintaining a constant energy supply while preserving consumer comfort and grid stability. The simulation results with various intermittent availability of resources using MKNS show a saving up to 50% for a variable energy demand user. The proposed method is general and can also be applied to various underdeveloped regions with similar consumer demand and statistics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".