Temperature Forecasting of Grain in Storage: An Improved Approach Based on Broad Learning Network

Temperature forecasting of grain in storage is crucial for timely granary temperature control, mitigating adverse effects of extreme temperatures on grain quality. Traditional machine learning methods struggle with stability and high error rates in grain storage temperature forecasting, while deep learning models are more accurate but time-consuming and have heavy parameters. To address these problems, an improved model with light weight and good accuracy is proposed in this paper, which broad learning network is combined with one-dimensional convolution module and multi-head self-attention mechanism (BLN-1DCNN-MHSA). Firstly, we employ a one-dimensional convolution module at the feature nodes of the model to extract local temporal correlations, compensating for temporal sequence learning limitations of the BLN. Secondly, a multi-head self-attention mechanism at the enhancement nodes to captures important features dependencies and global temporal correlations. Lastly, our model achieves better prediction through enhanced representation ability of model nodes. The results with real grain storage temperature data demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.341, 0.54%, 0.28, respectively, which represent more than 2 times improvement in accuracy compared to the BLN, and it also reduces training time by more than 90% compared with LSTM and Transformer models. Additionally, the generalization and robustness of the improved approach are demonstrated through promising results in a classification experiment on the MNIST dataset. In general, the model provides a certain feasibility for early warning of grain storage risks by predicting its temperature trends.