Data-driven prediction of greenhouse aquaponics air temperature based on adaptive time pattern network.

Abstract

Greenhouse aquaponics system (GHAP) improves productivity by harmonizing internal environments. Keeping a suitable air temperature of GHAP is essential for the growth of plant and fish. However, the disturbance of various environmental factors and the complexity of temporal patterns affect the accuracy of the microclimate time-series forecasting. This work proposed an Adaptive Time Pattern Network (ATPNet) to predict GHAP air temperature, which consists of deep temporal feature (DTF) module, multiple temporal pattern convolution (MTPC) module, and spatial attention mechanism (SAM) module. The DTF module has a wide sensory range and can capture information over a long-time span. The MTPC module is designed to improve model response performance by exploiting the effective temporal information of different environmental factors at different times. At the same time, the SAM can explore the correlations among different environmental factors. The ATPNet found that air temperature of GHAP has a strong correlation with other temperature-related parameters (external air temperature, external soil temperature, and water temperature). Compared with the best performance of three baseline models (multilayer perceptron (MLP), recurrent neural network (RNN), and Temporal Convolutional Network (TCN)), the ATPNet enhanced overall prediction performance for the following 24h by 7.44% for root mean squared error (RMSE), 2.53% for mean absolute error (MAE), and 3.15% for mean absolute percentage error (MAPE), respectively.