A data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing gated recurrent network

The dynamic load forecasting of scraper conveyer is one of the key problems that need to be solved in unmanned coal mining. The dynamic load forecasting system of scraper conveyer is a complex, multivariable, and nonlinear system, and there are coupling relations between every variable. It is very difficult to establish precise mathematic model. Therefore, based on rough set and the gated recurrent units (GRU), this study proposes a data-driven method for dynamic load forecasting of scraper conveyer based on rough set and multilayered self-normalizing GRU network. First, the rough set was applied to carry on for a variety of factors affecting load forecasting of scraper conveyer to optimize the model input, and the importance of each attribute for load of scraper conveyer was obtained. Then, a multilayered self-normalizing gated recurrent units (MS-GRU) model is proposed for the dynamic load forecasting of scraper conveyer. This model introduces scaled exponential linear units (SELU) activation function to squash the hidden states to calculate the output of the model, and the exploding and vanishing gradient problem are overcome in a stacked GRU neural network. Finally, an experiment is applied to verify the proposed model in this paper. The experimental results show that this article Compared with the existing methods, the model shows a higher accuracy rate 95.8%, which can well complete the prediction of the operating parameters of the shearer.