## 1 Introduction
A load forecasting model based on least square support vector machine and chaotic time series is proposed in [22], and the real-time price of power load and chaotic characteristics is considered. A short-term load forecasting method based on price-responsive load is proposed in [23], and the response of the aggregate load to price is modeled by a set of marginal utility curves and power consumption limitations. The price response factors are estimated using a generalized inverse optimization scheme. Power load time-series data is a non-stationary random process, which has the characteristics of quasi-periodicity, non-stationarity and nonlinearity. In order to improve the stationarization of power load time-series data, empirical mode decomposition (EMD) [24] and the frequency domain decomposition algorithm [25] are used to decompose the original load time series. A data-driven deep learning framework to load forecasting is proposed in [26], Box-Cox transformation is used to process data, and deep belief network is used for the load forecasting. A user-side load prediction method that integrates empirical mode decomposition and deep belief network is proposed in [27]; by decomposing the original load data into several eigenmode functions with different frequencies and amplitudes, the deep belief network is used to feature extraction and time-series prediction for each modal function. A load forecasting method based on ensemble empirical mode decomposition (EMMD) and Elman network is proposed in [28], the EEMD sample entropy was used to decompose the original power load sequence into a series of subsequences. A fault feature extraction method based on VMD optimized with information entropy is proposed in [29], and several intrinsic model functions were obtained by signal decomposition. Variable mode decomposition is used to generate 2D time frequency spectrograms from the various fault conditions of bearing in [30], and deep convolutional neural network is applied for fault diagnosis. With the development of deep learning theory in the field of time-series data processing, CNNs have an outstanding performance in comparison with traditional RNN models such as LSTM. It is believed that sequence tasks should be reconsidered with RNN and the CNN method. The CNN neural network is seen as a new solution for the sequence modeling tasks [31]. Furthermore, a temporal convolutional network (TCN) is proposed, which accelerates the feedback and convergence of the deep network through the setting of the residual unit, and it solves the degradation phenomenon caused by the increase in the network level [32]. A short-term load forecasting model for industrial customers based on the temporal convolutional network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed in [33]; the proposed method can extract the hidden information and long-term temporal relationships in the electrical load. In [34], convolution neural networks (CNNs) and gated recurrent unit (GRU) is combined for probabilistic residential load forecasting, and a deep model based on the mixture density network is used to directly predict probability density functions. An improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both univariate and multivariate scenarios is proposed in [35]; the modified algorithm establishes an accurate mapping between the selected variables and load through its self-feedback. In this paper, a hybrid load forecasting method based on variational modal decomposition (VMD), temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed, and the contributions of this paper are as follows: (1) Variation modal decomposition is used for the original electrical load decomposition with different frequencies.