# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 1 Introduction
With the vigorous research into deep learning for image recognition and speech recognition [17; 18; 19; 20; 21], deep learning technology has been introduced into load forecasting. At present, there are mainly two kinds of prediction models based on CNN and the long LSTM model. [PERSON] et al. [22] proposed a load short-term prediction algorithm based on the fuzzy method and convolution neural network. It used the deep learning CNN model to extract relevant important parameters and fuzzy logic to represent the one-dimensional time series. [PERSON] et al. [23] qualitatively analyzed the multi-energy load relationship, screened the influencing factors of load prediction based on data-driven analysis, and proposed a new multi-energy load prediction algorithm based on the CNN-BiGRU algorithm. A prediction model using LSTM was proposed by [PERSON] et al. [24], in which autocorrelation graph was used for extracting hidden features. [PERSON] et al. [25] proposed a hybrid convolutional neural network (CNN) with cascaded networks to forecast the daily peak load, and tested it with data from Taiwan. This method was superior to the traditional model. [PERSON] et al. [26] proposed a regularized deep neural network method for short-term power load forecasting of commercial buildings and used it to forecast the power load of two commercial buildings in Virginia (30 min and 24 h ago). [PERSON] et al. [27] proposed a Bi-LSTM network for short-term load forecasting. In [PERSON]'s work, the feature of time series was extracted by the multi-directional LSTM model, which improved the multi-dimensional expression ability of the time series features. [PERSON] [28] proposed a new multi-behavior LSTM model with bottleneck characteristics. This model combined the prediction behavior and weekly features by applying the bottleneck technology to the energy management system. [PERSON] et al. [29] proposed a new deep residual network for load prediction. The network proposed a new deep residual network to address the issue of vanishing and exploding gradients in neural networks and to improve forecasting accuracy. [PERSON] et al. [30] introduced a hybrid multi-task and multi-information fusion deep learning algorithm that takes into account short-term and long-term behavioral rules to achieve load forecasting. Similarly, the authors of [31; 32] developed deep learning models, but they faced challenges in modeling the temporal and spatial characteristics of power data. At present, the outstanding methods are mainly based on the deep learning model, which can automatically obtain the characteristics of the time series. However, the existing methods based on CNN are difficult to obtain the temporal correlation of the time series, and the existing models do not consider the multi-time scale characteristics of the load series. Although LSTM can extract temporal correlation, its feature extraction ability is weak. In addition, the importance of various factors affecting load power is different, and the existing methods do not distinguish these factors, which also affects the accuracy of prediction. In this paper, a multi-scale feature attention mixture network model (MFAMNet) was constructed to realize load forecasting, which makes full use of the time information and introduces the LSTM network into the model to extract the time correlation of the sequence. The multi-scale convolution neural network (CNN) is used to automatically extract the multi-scale features of the load, and a lot of information will not be lost in the dimensionality reduction. In addition, this paper constructs a two-branch attention mechanism to capture the important parameters of different influencing factors to improve the ability of the network for extracting effective features. The experimental results on two open test sets show that the proposed MFAMNet is superior to the existing SOTA load prediction methods.