Computational Methods for Residential Energy Cost Optimization in Smart Grids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it