Advanced Home Energy Management Using Proximal Policy Optimization with a Comprehensive Appliance Set
Bibliographic record
Abstract
Home Energy Management Systems (HEMS) are essential for optimizing household energy consumption and reducing costs, particularly in smart grids, where renewable energy sources and demand side management play a critical role. We propose an advanced HEMS framework that utilizes Proximal Policy Optimization (PPO), a reinforcement learning (RL) algorithm to address the challenges of managing energy consumption in realistic and dynamic environments. Our approach provides a comprehensive smart home environment by incorporating a wide range of household appliances, each with distinct patterns of energy consumption and operational constraints. This enhances the realism and practical relevance of the system, allowing cost savings while respecting user preferences and the limitations of the appliance. Simulations reveal that the proposed HEMS framework significantly improves energy savings, reduces costs, and enhances user satisfaction compared to other baseline methods, achieving 38% lower costs and 3% higher satisfaction of the energy level of electric vehicles (EV) than the Soft Actor-Critic based HEMS. These results highlight the effectiveness of RL and realistic environment modeling in the development of adaptive and efficient HEMS solutions, paving the way for more sustainable energy management practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".