Energy management for smart residential homes: A real-time fuzzy logic approach
Bibliographic record
Abstract
Energy management within smart residential homes is a long-standing challenge that involves effective scheduling of electric vehicle charging and discharging while utilizing available photovoltaic resources and efficiently drawing power from the electric grid to meet household energy demands. In this work, we propose a fuzzy logic-based real-time energy management control system from the perspective of an electric utility to achieve these objectives while simultaneously minimizing electricity costs for both the utility and customers, promoting reliable power grid operation, and mitigating distribution transformer overloading. The efficacy of the proposed energy management controller is evaluated on a secondary distribution system, and delivered results in computational time of just 52 ms. Further investigation is conducted on a large-scale power distribution test feeder, comprising diverse secondary distribution groups. The findings indicate that the proposed approach offers significant benefits to all stakeholders. • Addressed smart homes’ energy management challenges. • Proposed a fuzzy logic-based real-time control system. • System minimizes electricity costs and ensures grid reliability. • Reduces risks of transformer overloading. • Efficient: 52 ms computational time on secondary distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".