Enhancing Thermal Comfort in Solar-Integrated Buildings Using Proximal Policy Optimization
Bibliographic record
Abstract
The integration of artificial intelligence (AI) with renewable energy resources has significantly advanced the capabilities of modern building energy management systems, particularly in enhancing energy efficiency and indoor thermal comfort. In this study, we propose a reinforcement learning (RL)-based control strategy for optimizing zone-level temperature regulation in a multi-zone office building. The system employs a single agent trained using Proximal Policy Optimization (PPO) to manage variable air volume (VAV) reheat coils in each zone, while dynamically adjusting the upper and lower temperature dead bands. The primary objective is to enhance thermal comfort, maximize the utilization of on-site solar energy, and promote overall energy savings. The performance of the PPO agent is evaluated against a conventional rule-based controller (RBC). Simulation results demonstrate that the RL-based controller can maintain the average room temperature up to $1.36^{\circ} \mathrm{C}$ closer to the desired setpoint compared to the RBC and increase on-site solar energy utilization by up to $77 \%$ compared to the RBC. However, these improvements are accompanied by a increase in VAV energy consumption. The findings highlight a clear trade-off between enhanced occupant comfort and increased energy demand.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".