# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 2 Preliminaries and Problem Definition  
### Load Forecasting  
Load forecasting predicts the power needed to meet short-, medium-, and long-term demands. 1. The advantages of load forecasting are as follows: * It helps utility companies to better operate and manage supplies for their customers; * It is an important process that can increase the efficiency and profit of power generation and distribution companies. * It helps plan capacity and operation to provide a stable energy supply to all consumers [39; 40; 41]
2. The challenges of load forecasting are as follows: * The power load series is complex and shows various seasonality levels; hence, a given time load can be accommodated at the same time in a specific weekday for the same time load and the previous week as well as the previous time load; * Many important exogenous variables must be considered when forecasting power, especially those related to weather, making it difficult to achieve an accurate prediction [42; 43]. Several load forecasting studies have analyzed weather factors and power consumption patterns in various ways [44; 45; 46; 47; 48]  
The present study proposed that power demand forecasting includes special days (e.g., special holidays and official holidays) during the week and accounts for buildings with different power consumption patterns. In the former, the power consumption of on special day during the week is estimated to approximate that of the weekend. Table 1 shows the dates and days of the week for a special day of January, April, July, and October in Korea in the last three years (2017-2019). For the latter, we forecast medium- and long-term power demands for the B and T companies with different power consumption patterns based on Table 1.  
Figure 1 shows the average electricity usage for each day of the week for January (winter), April (spring), July (summer), and October (autumn) for company B for three years. The following observations are made from Figure 1:  
1. The electricity usage of company B for three years is in the order of summer (121 MW) \(<\) spring (154 MW) \(<\) autumn (159 MW) \(<\) winter (197 MW). It is less affected by weather than summer and winter, where air-conditioning usage is the highest. 2. It showed the highest electricity usage occurs during working hours (8 a.m. to 7 p.m.) from Monday to Friday (weekdays). Saturdays and Sundays (weekends) show that there is almost no electricity usage (below 50 MW). In addition, there was almost no power consumption during lunchtime from 12:00 to 1:00 p.m. compared with other hours on weekdays. 3. As a result of comparing the electricity usage for special days of company B for three years, the power consumption pattern is regular in April and July because there are no special holidays. In addition, in October, as special holidays are mostly from Monday to Friday, power consumption patterns are regular, similar to those of April and July. However, January had high electricity usage on Wednesdays and Thursdays, excluding special holidays (Monday, Tuesday, and Friday). Figure 2 shows the average electricity usage for each day of the week for January (winter), April (spring), July (summer), and October (autumn) for company T over three years. The following observations are made from Figure 2:  
1. The electricity usage of T company for three years is in the order of winter (488 MW) \(<\) spring (511 MW) \(<\) autumn (546 MW) \(<\) summer (605 MW). As company T is a livestock meat processing company, cooling is more important than heating, so it uses more electricity in spring, autumn, and summer than in winter. 2. The power consumption pattern of company T's weekend (Saturday to Sunday) is smaller than on weekdays, but fluctuations in electricity usage are more flexible at each time than those of