Forecast-Augmented Ensemble Control for Greenhouse Microclimate Regulation

초록

Highlights What are the main findings? center dot A forecast-augmented RF-GB-SVM ensemble controller achieved 0.83 degrees C temperature RMSE during 28 days of continuous greenhouse deployment. center dot Forecast integration enabled proactive irrigation suppression before rainfall events, reducing pump operating time by 18% without degrading soil moisture compliance. center dot The proposed ensemble controller achieved lower temperature-tracking error than the threshold-based and PID reference baselines under the adopted disturbance-consistent evaluation protocol. center dot Feature ablation and explainability analysis confirmed the dominant contribution of forecast-derived variables to anticipatory control performance. What are the implications of the main findings? center dot Low-cost edge AI hardware can support practical multi-variable greenhouse control without computationally intensive optimisation. center dot Forecast-driven feedforward control improves water-use efficiency and operational sustainability in greenhouse environments. center dot The proposed framework provides a deployable foundation for future adaptive and large-scale intelligent greenhouse systems.Highlights What are the main findings? center dot A forecast-augmented RF-GB-SVM ensemble controller achieved 0.83 degrees C temperature RMSE during 28 days of continuous greenhouse deployment. center dot Forecast integration enabled proactive irrigation suppression before rainfall events, reducing pump operating time by 18% without degrading soil moisture compliance. center dot The proposed ensemble controller achieved lower temperature-tracking error than the threshold-based and PID reference baselines under the adopted disturbance-consistent evaluation protocol. center dot Feature ablation and explainability analysis confirmed the dominant contribution of forecast-derived variables to anticipatory control performance. What are the implications of the main findings? center dot Low-cost edge AI hardware can support practical multi-variable greenhouse control without computationally intensive optimisation. center dot Forecast-driven feedforward control improves water-use efficiency and operational sustainability in greenhouse environments. center dot The proposed framework provides a deployable foundation for future adaptive and large-scale intelligent greenhouse systems.Abstract Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. 'Madora F1') over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 degrees C during field deployment. For reference, RMSE values of 3.21 degrees C and 1.94 degrees C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94. 1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation.

키워드