Computational Methods for Residential Energy Cost Optimization in Smart Grids
Bibliographic record
Abstract
A smart power grid transforms the traditional electric grid into a user-centric, intelligent power network. The cost-saving potential of smart homes is an excellent motivating factor to involve users in smart grid operations. To that end, this survey explores the contemporary cost-saving strategies for smart grids from the users’ perspective. The study shows that optimization methods are the most popular cost-saving techniques reported in the literature. These methods are used to plan scheduling and power utilization schemes of household appliances, energy storages, renewables, and other energy generation devices. The survey shows that trading energy among neighborhoods is one of the effective methods for cost optimization. It also identifies the prediction methods that are used to forecast energy price, generation, and consumption profiles, which are required to optimize energy cost in advance. The contributions of this article are threefold. First, it discusses the computational methods reported in the literature with their significance and limitations. Second, it identifies the components and their characteristics that may reduce energy cost. Finally, it proposes a unified cost optimization framework and addresses the challenges that may influence the overall residential energy cost optimization problem in smart grids.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".