Short-term coal storage forecasting of thermal power plant for power grid dispatching based on CNN-LSTM neural network

The univariate single-step forecasting of coal storage for power plant with traditional regression fitting cannot meet the need of optimal dispatching for power grid, for this problem, CNN(Convolutional Neural Networks)and LSTM(Long Short-Term Memory) neural network are combined, and a CNN-LSTM neural network forecasting model is proposed, which uses CNN's good extraction capability and LSTM's special memory forecasting function to realize accuracy forecasting of future coal storage for power plant. In order to make the forecasting results more consistent with the actual coal storage, further optimization is carried out based on the existing forecasting results. Case verification results show that, compared with the traditional ARIMA(AutoRegressive Integrated Moving Average) model and single LSTM neural network model, the proposed model obtains better effect, and the forecasting accuracy after optimization is further improved. © 2021, Electric Power Automation Equipment Press. All right reserved.