# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 1 Introduction  
With the development of smart grid technology, massive deployment of the advanced metering infrastructure (AMI) and all kinds of monitoring systems generate and accumulate a large amount of data [1]. The smart meter is an important part of AMI and it can obtain the precise user electricity load within a certain time interval (such as 15 min, 30 min, etc.) [2]. Compared with many traditional systems, the smart meter is able to generate more data in a shorter time period, which means that most data are collected without deep analysis. It is important to mine the values of these data fully; for example, the user electricity behaviors can be mined via smart meter data and the accuracy of load forecasting can be improved according to user electricity behaviors. Load forecasting has always been a key operation and planning approach of power systems, as it affects many decisions relating to power systems such as economic dispatching, automatic generating control, security evaluation, maintenance scheduling, and energy commercialization [3; 4]. Accurate load forecasting can start and stop a power system generator set both economically and reasonably, and plays an important role in maintaining the security and stability of the power grid operation, maintaining the normal production and life of society, and effectively reducing the generation costs [5]. Load forecasting can be divided into four categories; super short-term, short-term, medium-term, and long-term based on different purposes, and their time ranges of forecasting are within 1 h in the future, one day or a week, one month to one year, and 3 to 5 years in the future, respectively [6; 7; 8]. This paper mainly makes a short-term load forecasting of user electricity load. In recent years, there have been many studies about short-term load forecasting, regarding the influence factors of load forecasting, accumulated temperature effect caused by continuous high temperatures during the summer, demand side management, and the impact which the renewable energy grid has on electricity load, considered respectively in [9; 10; 11]. In the construction of the forecasting model, the forecasting models for load demand in buildings are presented by using the time-series approach [12]. A power load forecasting model based on support vector machine and particle swarm optimization is proposed in [13]. A Least Squares Support Vector Machine (LS-SVM) forecasting model based on K-means is proposed in [14]. Multiple linear regression, stepwise linear regression, and neural network methods are introduced in [15], which also improves the neural network prediction model to increase the forecasting accuracy. However, classical load forecasting is limited by the lack of user load data, as it generally makes direct load forecasting of system data [16]. However, the relationship between electricity loads of different users is not considered, which affects the load forecasting accuracy. A smart meter can record a user's consumption details and classify user loads into clusters, which can effectively mine the relationship between electricity loads of different users (mine the relationship between electricity behaviors of users). In the analysis of user electricity behaviors, medium-term and long-term analyses are made about user electricity behaviors in [17], and appropriate algorithms and the optimal clustering number of mining user electricity behavior similarity are compared in depth. User electricity loads are clustered according to the similarity between user electricity load curves in [18; 19; 20], through the analysis of the clustered load curves, a reasonable real-time electricity price strategy is put forward to achieve \"peak load shifting\" of the load curves. The loads are clustered according to user electricity behaviors to estimate the electricity consumption habits and capacities of different users, and an electricity price strategy based on user electricity behaviors is proposed accordingly [21; 22; 23].