| Citation: |
KONG Wen, CHE Quan, ZHAO Huirong, PENG Daogang. Short-term Prediction of Coal Stock in Power Plant Based on Singular Spectrum Analysis and Long Short-term Memory Neural Network[J]. INFORMATION AND CONTROL, 2020, 49(6): 742-751. DOI: 10.13976/j.cnki.xk.2020.9484
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In view of the low accuracy of coal storage predict, ion in thermal power plants, we propose a multi-variable and multi-step prediction model based on singular spectrum analysis (SSA) and long short-term memory (LSTM) neural network. Considering the influence of historical data and temperature of power plant, we use singular spectrum analysis or wavelet analysis to smooth the data, remove the noise components from the data, and use it as the input vectors of LSTM neural network for simulation test. The results show that compared with the combination of back propagation (BP) neural network, recurrent neural network, wavelet analysis and LSTM, the multi variable and multi-step prediction model based on singular spectrum analysis and long short time memory neural network adopted has higher accuracy.
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