Prediction of Market Demand for Solar Street Lights in China Based on CNN
Since the data of demand in the solar street lamp market in China,the prediction performance of traditional artificial neural networks is unsatisfactory.The convolutional neural network(CNN)uses filters to make the neurons of adjacent layers partially connected,and the neuron parameters of the same feature plane are the same,so that all neurons can share parameters,with excellent performance and high prediction accuracy.The convolution neural network was used to predict the market demand for solar street lights in China.The results show that the average prediction error is only 0.958 8%,18.065 3%less than 1.170 2%of SVM,and 11.402 7%less than 1.082 2%of random forest.The model was used to predict the market demand for solar street lights in China from 2023 to 2027,and analysis shows that this prediction result has high reliability.
NETL
NSTL
万方数据
