RESEARCH ON SOLAR HEATING LOAD FORECASTING BASED ON CNN AND BILSTM NEURAL NETWORK MODEL
Aiming at the phenomenon of energy waste caused by the mismatch between heat supply and demand in solar heating system,a short-term heat load forecasting model based on convolutional neural network-bidirectional long short-term memory neural network is proposed.Firstly,the data is cleaned to make the data accurate and complete.Secondly,the input features are screened according to the Pearson correlation coefficient.Finally,a convolutional neural network-bidirectional long-term and short-term memory neural network model is established based on its spatial-temporal characteristics.After detailed comparison and analysis with the single neural network model,the length of the memory neural network and the two-way long short-term memory neural network,the results show that the model has a significant improvement in accuracy compared with the traditional neural network model,which verifies the effectiveness of the model in the prediction of solar heating load.
solar heatingconvolutional neural network(CNN)long short-term memory(LSTM)thermal loadneural network model
万方数据
维普
