*3.1. Time Data*

As time is a very critical factor in the trends of electric energy consumption, we consider all variables that express time such as month, day, hour, day of the week, and holiday. Table 3 shows a list of the time factors we considered as input variables. Herein, month, day, and hour have a sequence form. It is difficult to reflect periodic information in machine learning algorithms when data are in a sequential format. Therefore, we enhanced the data to 2-dimensional data through the periodic function [36]. Table 4 summarizes some regression statistics of 1-dimensional, 2-dimensional, and 1-dimensional + 2-dimensional time factors. The table shows that 1-dimensional + 2-dimensional space data can represent the time factor most effectively. Therefore, we use both 1-dimensional data and continuous 2-dimensional data to represent time factor.


**Table 3.** Input variables of time factors.


**Table 4.** Regression statistics of time data representation.
