Short-term Electricity Load Forecasting Based on CEEMDAN Decomposition and TCN-BIGRU Model
In order to conduct an in-depth analysis of the complex short-term power load variation patterns and their long-term correlations,the paper proposes a fusion model combining the temporal convolutional network(TCN)based on the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and the bidirectional gated recurrent unit(BiGRU).The CEEMDAN method is employed to decompose the complete power load data into multiple intrinsic mode function sequences and a residual term sequence.The extended convolution strategy of the TCN model is utilized to extract the features of the time series.The nonlinear fitting ability of the BiGRU is combined to further extract the nonlinear features of the TCN training output.The experimental results demonstrate that,in the short-term power load forecasting,the determination coefficient of the proposed model for prediction is 96.6%,the mean absolute percentage error is 3.82%,and the mean absolute error is 45.5.All these indicators are superior to those of the contrast models.
Short-term electric load forecastingModel decompositionTemporal convolutional networksBidirectional gated loop cell
万方数据
维普
