Efficient Greenhouse Temperature Control with Data-Driven Robust Model Predictive Control

TL;DR

This paper introduces a data-driven robust model predictive control method for greenhouse temperature regulation that accounts for weather forecast uncertainties, leading to improved energy efficiency and temperature stability.

Contribution

It develops a novel robust MPC approach using thermal modeling and support vector clustering to handle weather uncertainties in greenhouse temperature control.

Findings

Outperforms rule-based control, certainty equivalent MPC, and robust MPC in case studies.

Uses support vector clustering to accurately capture weather uncertainty sets.

Achieves better temperature regulation with reduced energy consumption.

Abstract

Appropriate greenhouse temperature should be maintained to ensure crop production while minimizing energy consumption. Even though weather forecasts could provide a certain amount of information to improve control performance, it is not perfect and forecast error may cause the temperature to deviate from the acceptable range. To inherent uncertainty in weather that affects control accuracy, this paper develops a data-driven robust model predictive control (MPC) approach for greenhouse temperature control. The dynamic model is obtained from thermal resistance-capacitance modeling derived by the Building Resistance-Capacitance Modeling (BRCM) toolbox. Uncertainty sets of ambient temperature and solar radiation are captured by support vector clustering technique, and they are further tuned for better quality by training-calibration procedure. A case study that implements the carefully chosen uncertainty sets on robust model predictive control shows that the data-driven robust MPC has better control performance compared to rule-based control, certainty equivalent MPC, and robust MPC.