# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 2 Analysis of User Electricity Behavior  
Based on different electricity equipment and preferences, users have various behaviors. Not considering the differences between these user behaviors will seriously restrain the improvement of load forecasting accuracy. With the gradual popularization of smart meters, detailed electricity information from users can be accurately obtained, and it is of great significance to improve load forecasting accuracy via in depth studies of user electricity behaviors and by mining the relationship between user behaviors. Therefore, user behaviors are analyzed in depth in this section and related works about load forecasting are demonstrated below. This paper mainly adopts the smart meter data of Irish households from the Irish Social Science Data Archive (ISSDA) [32]. These data recorded the electricity load of users from 14 July 2009 to 31 December 2010 every 30 min. This paper randomly selects 3000 households and uses three months of their smart meter data from 20 July 2009 to 20 October 2009 as the experimental data. Because these 3000 households were selected randomly, the results can reflect the whole situation of electricity usage. The sum of the electricity load from 3000 households is regarded as the system load in the experiment. The load curves of the system and a domestic user on the same day are shown in Figure 1. It can be seen that the load curve shapes of the system and single user have a big difference; the system load forecast cannot reflect the future electricity load of a singular user. Therefore, it can improve the accuracy of load forecasting by aggregating the user load forecast to obtain the system load forecast. The electricity load curves of two different users on the same day are shown in Figure 2. It can be seen that the load curves of different users are not the same; this is due to the fact that every user owns different electrical equipment, and has different usage times and preferences. Therefore, the users with similar electricity behaviors can be classified into the same cluster by in depth mining of their behaviors, and the behaviors of users from different clusters are not the same. Figure 1: Load curves of the system and one domestic user on 18 August 2009. Figure 2: Load curves of two different domestic users on 16 September 2009. The day load curves of a domestic user over the course of one week are shown in Figure 3. It can be seen that the electricity loads of the same user is different at various times, therefore, the differences between user habits at various times should be considered during load clustering. In load forecasting, date type is usually taken into consideration as an influencing factor, so date type should be introduced in load clustering. It can be said that the reasonable clustering of user loads is very important and necessary for load forecasting. K-means is adopted to cluster loads in this paper. Smart meter data can probably be lost during transmission, so the average of the first three loads with the same date type and time is used to supplement the missing data reasonably. Mining the similarity between user electricity behaviors has to do with their load curve shape and it has nothing to do with the specific electricity consumption. Therefore, the typical load data of every date type should be normalized before clustering. Date type is considered in this paper and it is divided into ordinary workdays, day before holidays, and holidays. The average electricity loads of three types of dates within one month is regarded as the typical day load data of the respective date type. The typical day loads of every date type are then normalized and the normalized data are joined to participate in the load clustering as the typical loads of users; there are 48 \(\times\) 3 = 144 dimensions (there are 48 load values every day and the joined load values of the three date types are 144) based on which load clustering is performed to mine the similarity between user behaviors.