Energy storage management in smart homes based on resident activity of daily life recognition
Bibliographic record
Abstract
Recently, home energy storage system is emerging as one of the main driving forces to prompt the development of the future smart grid. By leveraging time-based electricity pricing, the home energy storage system can store energy during off-peak periods and supply energy to residential customers during on-peak periods, such that the stress on main power system can be relieved. Yet, an efficient use of home energy storage system is still a challenging issue, due to the nonlinear properties of the battery in terms of energy conversion loss and shortened battery life, and the randomness in residential energy demand. In this paper, we investigate the utilization of smart home monitoring and communication technologies to recognize the resident activity of daily life (ADL) and propose a non-homogeneous hidden Markov model (NHMM) to characterize the residential energy demand. An optimal energy storage management problem is formulated by taking into account the NHMM and nonlinear battery properties. This problem belongs to a class of adaptive stochastic control problems in smart grid with nonlinear value functions. In order to solve this problem efficiently, piecewise linear approximation is applied to the energy conversion function, and a state-dependent multi-threshold policy is proposed and proved to be optimal. The performance of the proposed energy management scheme is evaluated via a case study based on CASAS smart home dataset collected in real life by Washington State University. Numerical results indicate that our proposed energy storage management scheme can achieve energy cost savings, in comparison with existing schemes with uniform and non-uniform discharging profiles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".