Sustainable Greenhouse Management: A Comparative Analysis of Recurrent and Graph Neural Networks

The integration of photovoltaic (PV) systems into greenhouses not only optimizes land use but also enhances sustainable agricultural practices by enabling dual benefits of food production and renewable energy generation. However, accurate prediction of internal environmental conditions is crucial to ensure optimal crop growth while maximizing energy production. This study introduces a novel application of Spatio-Temporal Graph Neural Networks (STGNNs) to greenhouse microclimate modeling, comparing their performance with traditional Recurrent Neural Networks (RNNs). While RNNs excel at temporal pattern recognition, they cannot explicitly model the directional relationships between environmental variables. Our STGNN approach addresses this limitation by representing these relationships as directed graphs, enabling the model to capture both spatial dependencies and their directionality. Using high-frequency data collected at 15-minute intervals from a greenhouse in Volos, Greece, we demonstrate that RNNs achieve exceptional accuracy in winter conditions (R^2 = 0.985) but show limitations during summer cooling system operation. Though STGNNs currently show lower performance (winter R^2 = 0.947), their architecture offers greater potential for integrating additional variables such as PV generation and crop growth indicators.

Short-Term Predictability of Photovoltaic Production over Italy

Photovoltaic (PV) power production increased drastically in Europe throughout the last years. About the 6% of electricity in Italy comes from PV and for an efficient management of the power grid an accurate and reliable forecasting of production would be needed. Starting from a dataset of electricity production of 65 Italian solar plants for the years 2011-2012 we investigate the possibility to forecast daily production from one to ten days of lead time without using on site measurements. Our study is divided in two parts: an assessment of the predictability of meteorological variables using weather forecasts and an analysis on the application of data-driven modelling in predicting solar power production. We calibrate a SVM model using available observations and then we force the same model with the predicted variables from weather forecasts with a lead time from one to ten days. As expected, solar power production is strongly influenced by cloudiness and clear sky, in fact we observe that while during summer we obtain a general error under the 10% (slightly lower in south Italy), during winter the error is abundantly above the 20%.