# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 1 Introduction  
Accurately predicting the power demand is very important for the stable operation of power systems with fluctuating power demands. The scale of the electric grid industry has grown with the shift of the social environment to a highly industrialized and information-oriented society. The amount of air-conditioning and heating equipment used by general consumers has rapidly increased. Accordingly, electricity consumption patterns change with changes in weather and specific days (e.g., regular holidays and temporary holidays). These changes require accurate and stable predictions, and an accurate analysis of the power load patterns is essential. The power load pattern has similar characteristics for all days of the week, but may vary for special days. For example, the power load pattern on a Wednesday is affected by the working day or a holiday on the previous day. Therefore, simply classifying the power load patterns by the day of the week is not appropriate; hence, prediction methods that can classify patterns according to power consumption data characteristics are necessary [1; 2; 3]. Power consumption patterns are periodically assessed by analyzing customer usage. These patterns are considered almost hourly, daily, weekly, monthly, and yearly. Electric power load forecasting is classified according to the duration of the planning period of the utility: short-termload forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF) [4; 5; 6]. The LTLF predicts the power demand for more than one year and is applicable to power systems and long-term network planning. Essentially, two approaches are available for this purpose:  
1. The peak load approach is used to find the trend curve obtained by plotting the past values of the annual peaks against the years of operation. 2. The energy approach aims to forecast annual sales, including annual energy sales, to different classes of customers (e.g., residential, commercial, industrial), which can then be converted to the annual peak demand using the annual load factors. MTLF predicts the electricity demand for one month to one year, between the LTLF and STLF. Meanwhile, STLF estimates the power demand for one hour to one week. It has a scheduling function that determines the highest economic commitment of the power generation source. STLF also provides the system dispatcher with up-to-date weather forecasts to ensure that the system can be operated both economically and stably. The electricity forecasting for building energy consumption is affected by weather (temperature, dew point, humidity, wind speed, wind direction, sky cover, and sunshine), time factors (the day of the week, the hour of the day holidays), and customer classes (residential, commercial, and industrial). Existing studies on building energy consumption can be divided into three categories [7; 8]:  
1. White box-based approaches, also named \"physics-based models\" require detailed physical information of complex buildings [9; 10]. Owing to these characteristics, although the forecasting accuracy is high, a high computational cost is required for the simulation. Recently, there have been a series of attempts to simplify white box-based approaches. However, this simplification is prone to errors and generally overestimates the energy savings of buildings [11; 12; 13]. There are several tools, such as DOE-2, EnergyPlus, BLAST, TRYSYS, and ESP-r, which aid white box-based approach [14]. 2. Black box-based approaches are also commonly referred to as \"data-driven models\". These approaches rely on time-series statistical analyses and machine learning to assess and forecast electricity consumption [15; 16; 17]. Data-driven models are divided into three categories: * Conventional models refer to exponential smoothing (ES) [18], moving average (MA) [19], statistical regressions [20], auto-regressive (AR) models [21], genetic algorithms (GA) [22], and fuzzy-based models [23; 24]. They provide a good balance between forecasting accuracy and implementation simplicity.