Hiroki Manabe, Toshie Manabe, Yuki Honda, Yoshihiro Kawade, Dan Kambayashi, Yoshiki Manabe, Koichiro Kudo
Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend
BMC infectious diseases
study addresses drawbacks traditional methods used meter coefficient analysis low accuracy long processing time new method based nonparametric analysis using back propagation bp neural network proposed overcome limitations study explores classification pattern recognition capabilities bp neural network analyzing nonparametric model optimization methods model construction study uses united kingdom domestic appliancelevel electricity datasets meter readings related data training testing proposed model nonparametric analysis model used data preprocessing feature extraction normalization obtain training testing datasets experimental tests compare proposed nonparametric analysis model based bp neural network traditional least squares method lsm results demonstrate proposed model significantly improves accuracy indicators mean absolute error mae mean relative error mre compared lsm method proposed model achieves mae 0025 mre 132 testing dataset lsm method mae 0043 mre 256 dataset therefore proposed nonparametric analysis model based bp neural network achieve higher accuracy meter coefficient analysis compared traditional lsm method study provides novel nonparametric analysis method practical reference value electricity industry energy metering load forecasting