Power Management in Smart Buildings Using Reinforcement Learning
Bibliographic record
Abstract
This paper proposes a novel framework of power management system (PMS) using reinforcement learning (RL) in presence of a thermal energy storage system (TESS) and a battery energy storage system (BESS) to achieve peak load shaving and compensate for BESS limitations. This paper focuses on using RL approach to define the optimal charging/discharging schedule of TESS and BESS in PMS. The optimization problem is formulated as Markov decision process (MDP) and then solved by Q-learning algorithm. The efficacy of the proposed PMS framework is demonstrated by using power consumption data of a campus building. Moreover, the optimal solution obtained by RL is validated and compared with metaheuristic optimization approaches such as particle swarm optimization (PSO). Results show the effectiveness of the proposed PMS using RL to define the optimal operation schedule of energy storage systems (ESSs) while reducing 42.2% of the required BESS capacity.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".