为降低负荷序列的复杂性, 利用EMD分解方法得到不同的分量. 为降低训练时间和减小分量逐个预测所带来的累计误差, 利用分量过零率大小将分量重构为高频分量和低频分量, 利用TCN模型预测负荷的高频分量, 利用极限学习机ELM预测负荷低频分量. 通过实验将所提模型EMD-TCN-ELM分别与3个单模型TCN、ELM、LSTM和3个混合模型EMD-TCN、EMD-ELM、EMD-LSTM比较, 其MAPE分别降低0.538%, 1.866%, 1.191%, 0.026%, 1.559%, 0.323%, 所提模型的预测精度最高. 且所提模型在预测精度前3的模型中训练时间最短, 验证了所提模型在负荷预测精度和训练时间方面的优越性.
For lower complexity of the load sequence, the empirical mode decomposition (EMD) method is used to obtain different components. For shorter training time and a smaller cumulative error caused by component forecasting one by one, the components are reconstructed into high-frequency and low-frequency ones according to the zero-crossing rate of the components. The high-frequency components of the load are forecasted by the temporal convolutional network (TCN) model, whereas the low-frequency ones are forecasted by the extreme learning machine (ELM). The proposed EMD-TCN-ELM model is compared with three individual models TCN, ELM, and long short-term memory (LSTM) and three mixed models EMD-TCN, EMD-ELM, and EMD-LSTM through experiments, and its mean absolute percentage error (MAPE) is reduced by 0.538%, 1.866%, 1.191%, 0.026%, 1.559%, and 0.323%, respectively. The forecasting accuracy of the proposed model is also the highest. Additionally, the proposed model has the shortest training time among the top three models in forecasting accuracy. The above results verify the superiority of the proposed model in load forecasting accuracy and training time.