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
维普
万方数据
