Real-Time VR-in-the-Loop Model Predictive Control for Greenhouse Temperature Regulation with LSTM Forecasts
Bibliographic record
Abstract
This paper presents a real-time greenhouse temperature control system integrating Model Predictive Control (MPC) with deep learning forecasts and a Virtual Reality (VR) human-in-the-loop interface. The indoor climate is regulated by an MPC algorithm that optimizes heating and ventilation actions using predictive models of the greenhouse and upcoming weather conditions. Long Short-Term Memory (LSTM) neural networks provide short-term forecasts of external temperature, solar radiation, and wind, enabling the MPC to anticipate disturbances. A VR interface allows expert users to immerse in a virtual greenhouse environment and dynamically adjust the temperature setpoints in real time. By combining automated predictive control with human expertise, the system aims to improve climate regulation, energy efficiency, and adaptability to unforeseen conditions. Simulation case studies compare a baseline MPC-driven climate control against scenarios where a human operator in VR overrides setpoints during critical events. The results indicate that VRbased setpoint adjustment can enhance temperature stability and constraint compliance, with minor trade-offs in energy consumption. This work demonstrates the potential of integrating advanced control, AI-based forecasting, and immersive VR technology for next-generation smart greenhouse management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".