Machine learning-based greenhouse temperature forecasting model with structural and geospatial features

초록

Accurate forecasting of greenhouse internal temperature is important for proactive climate management, energyefficient operation, and stable crop production. This study evaluates a GRU-based forecasting model that augments conventional environmental measurements with deterministic structural and solar-geometry descriptors, including latitude, longitude, roof slope, roof azimuth, solar altitude, solar azimuth, and roof-specific incidence angles. Using winter data collected from an experimental greenhouse with spatially distributed sensors and an on-site weather station, we compared a baseline GRU with an improved GRU incorporating the added descriptor set. The improved model increased the average R2 from 0.622 to 0.662 and reduced the average RMSE from 1.935 to 1.801 degrees C across sensor locations. A repeated rolling evaluation using 7-day training and 1-day testing windows showed consistent improvement over the available dataset, supported by paired Wilcoxon tests. These results indicate that structural and solar-geometry descriptors can improve short- to medium-term greenhouse temperature forecasting under the present single-site winter conditions. Broader validation across seasons and sites remains necessary.

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