Constrained Deep Reinforcement Learning for Energy Management of Community Multi-Energy Systems
Bibliographic record
Abstract
This study proposes an intelligent energy management framework based on Constrained Deep Reinforcement Learning (CDRL) tailored for Community Multi-Energy Systems (CMES), aiming to enhance energy efficiency and mitigate carbon emissions by coordinating the optimization of various energy vectors in a synchronized manner. Initially, a multidimensional, continuous, stochastic action and state space constrained Markov Decision Process (CMDP) is employed to model the sequential multi-energy scheduling problem. Subsequently, an intelligent agent is iteratively trained through interactions with the environment, utilizing constrained policy Policy Optimization (CPO) algorithm, to learn the near-optimal system schedule while ensuring near-zero constraint violations. The effectiveness of the proposed approach is then substantiated through numerical validation conducted on authentic datasets, which incorporate electricity consumption records from fifteen households in London, Ontario, Canada, covering the years from 2014 to 2016. The results underline the potential of CDRL in enhancing sustainable energy management practices, offering insights into scalability and application in broader contexts.
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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 | 0.003 |
| 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) | 0.001 | 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".