# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 1 Introduction  
With the arrival of the new energy era, there has been a sharp increase in demand for load use, and the characteristics of load fluctuations have also changed. To ensure the safety and efficiency of the power system, the role of load forecasting is becoming increasingly important. Short-term load forecasting is utilized to forecast the load value for the next day or several consecutive days. Its forecasting results can be used for economic load dispatching, equipment maintenance, water, heat and electricity coordination, and more. Improving load forecasting accuracy will help ensure a balance between power supply and demand, and improve the economy of economic dispatching and power generation equipment utilization. With the continuous advancement of smart grid data acquisition technology, the dimension of load characteristics is becoming larger, and the volatility and nonlinearity of load are becoming stronger. All of these factors increase the difficulty of load forecasting [1]. Short-term power loads show strong randomness and volatility due to various factors such as climate, economic, and residential electricity consumption behavior. This increases the difficulty of load forecasting [2]. Currently, short-term load forecasting methods can be classified into three categories: mathematical statistics-based forecasting models, traditional machine learning-based forecasting models, and deep learning-based forecasting models. Load forecasting based on mathematical-statistical models includes multiple linear regression, Kalman filter, exponential smoothing method, and more [3]. Mathematical-statistical models have a clear statistical relationship between time, load, influencing factors, and past time load. The mathematical-statistical model is simple, and its prediction speed is fast. However, only simple and smooth time series can be suitable for this prediction method. For complex nonlinear load series, random factors can destroy the original prediction criteria of the model, leading to inaccurate prediction results [4]. In traditional machine learning models, support vector regression (SVM), fuzzy systems, decision trees (DT), and linear regression (LR) are commonly used to predict power consumption [5; 6; 7; 8]. [PERSON] et al. [9] developed an improved random forest algorithm for weekly power prediction, while [PERSON] et al. [10] developed a hybrid model that combined mode decomposition and support vector regression for power consumption prediction. [PERSON] et al. [11] proposed a hybrid model combining wavelet transform (WT) and support vector machine (SVM) for power load estimation. [PERSON] et al. [12] proposed a regional mixed STLF model that used SVM and the grasshopper optimization algorithm to estimate appropriate model parameters for load forecasting. [PERSON] et al. [13] proposed a hybrid method based on empirical mode decomposition and extreme learning machine (ELM) to predict residential load based on smart meter data and compared it with traditional machine learning models to verify its effectiveness. [PERSON] et al. [14] proposed a short-term prediction algorithm based on the optimized ELM algorithm. [PERSON] et al. [7] built a forecasting algorithm based on the fuzzy system. [PERSON] et al. [15] used the fuzzy neural model to predict the hourly load distribution before the day. The model took into account the time distribution of temperature, and the parameters of the model do not need to be set in advance. [PERSON] et al. [16] proposed a meta-learning algorithm for automatic distribution systems. The algorithm included three parts: extraction of features, preparation of model parameters, and online recommendation of models. Machine learning models perform well on simple datasets, but due to the collinearity of the independent variables of power consumption, these models have insufficient power consumption prediction ability. In addition, with the increase of data, these models usually have the problem of over-fitting.