Towards Optimal Planning and Scheduling in Smart Homes
Bibliographic record
Abstract
This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy trading among the smart homes in a community microgrid. However, P2P trading potentially results in an unfair cost distribution among the participating households. To the best of our knowledge, the ECO-Trade algorithm is the first near-optimal cost optimization algorithm which considers the unfair cost distribution problem for a Demand Side Management (DSM) system coordinated with P2P energy trading. It also solves the time complexity problem of our previously proposed optimal model. Our results show that the solution time of the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Furthermore, we analyze the solutions and identify that the algorithm sometimes gets trapped at a local minimum because it alternately sets the microgrid price and quantity as constants. Finally, we describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".