基于VMD和TCN的多尺度短期电力负荷预测

准确的电力负荷预测对于保证电力系统的稳定运行起着重要作用。针对传统短期电力负荷预测方法预测精度低，模态分解后未考虑子序列融合等问题，提出一种基于变分模态分解(VMD)和时域卷积网络(TCN)的多尺度短期电力负荷预测方法。首先利用VMD将电力负荷数据分解为若干个子序列，解决电力负荷数据的非线性和随机性等问题；再利用TCN对若干个序列采用不同时间尺度进行训练；最后利用全连接网络(FC)对各时间尺度的子序列进行融合，实现短期电力负荷预测，提升预测精度。实验结果表明，该方法相较于VMD和改进的长短时记忆网络(LSTM)相结合的传统预测方法，其均方根误差下降40%，曲线拟合程度提升1.1%。

Multi-Scale Short-Term Load Forecasting Based on VMD and TCN

Accurate power load forecasting plays an important role in ensuring the stable operation of the power system. In view of the low precision of traditional short-term power load forecasting methods, the sub-sequence fusion problem is not considered after modal decomposition, this paper proposes a multi-scale short-term power load forecasting based on variational mode decomposition (VMD) and temporal convolutional network (TCN) methods. First, VMD is used to decompose the power load data into several sub-components to solve the problems of non-linearity and randomness in the power load data, then TCN is applied to train several components with different time scales, finally a fully connected network is used to analyze each component. Time-scale decomposition signals are fused to realize short-term power load forecasting and improve forecasting accuracy. The experimental results show that the root mean square error (RMSE) is reduced by 40% and the curve fitting is improved by 1.1% compared with the traditional prediction method of VMD and improved long-short-term memory network.