Abstract:To effectively improve the accuracy of power load forecasting and in allusion to such characteristics of power load as nonlinearity, non-stationary and time sequence, a multi-frequency combination power load forecasting model, in which the Convolutional Neural Network (abbr. CNN) and the Bidirectional Gated Recurrent Unit (abbr. BiGRU) and the Multiple Linear Regression (abbr. MLR were mixed, was proposed. Firstly, in the proposed model the correlation degree analysis was utilized to obtain similar days and their loads were constituted new data series, meanwhile the variational mode decomposition (abbr. VMD) was used to decompose the obtained data series and reconstruct into high and low frequencies. As for the high-frequency component the CNN-BiGRU model was used for the prediction; and for the low-frequency component the MLR was used. Finally, superposing the predicted results obtained by above mentioned two models the final predicted results could be obtained. Based on the real data of Australia in 2006, a short-term load forecasting was performed. Simulation results show that comparing with other network models, by use of the proposed model the forecasting results possess higher prediction accuracy and fitting ability.
标题：基于变分模态分解的卷积神经网络−双向门控循环单元−多元线性回归多频组合短期电力负荷预测
英文标题：Multi-Frequency Combination Short-term Power Load Forecasting with Convolutional Neural Networks - Bidirectional Gated Recurrent Unit-Multiple Linear Regression based on Variational Mode Decomposition
