# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 1 Introduction
However, they have shown significant limitations in their ability to model nonlinear data patterns and the forecasting horizon. * Classification-based models applied to electric power load forecasting are k-nearest neighbors (k-NN) [25] and decision trees (DT) [26]. Although both are intuitive models with high predictive accuracy, they are limited owing to their need for a comprehensive set of input data. * Artificial intelligence models have been studied for many years and are generally referred to as machine learning and deep learning [27]. Some of the most popular artificial intelligence models are support vector machine (SVM) [28], artificial neural networks (ANN) [29], deep neural network (DNN) [30], and long short-term memory (LSTM) [31]. These models-based forecasting algorithms lead to less operator-dependent and more versatile methods in terms of data usage, with much higher forecasting accuracy. 3. Grey-box-based approaches have also been named \"hybrid-based models\" which are a combination of white box and black box models [32; 33; 34; 35]. These models have the advantage of using improved single data-driven models with optimization, or a combination of several machine learning algorithms. However, these approaches have the shortcoming of computational inefficiency because these approaches involve uncertain inputs and complex interactions among elements and stochastic occupant behaviors [36; 37; 38]. The power consumption pattern may vary depending on the customer class as electric utilities usually serve different types of customers (i.e., residential, commercial, and industrial customers). The power consumption of residential buildings is low compared to those of commercial and industrial buildings and is not significantly affected by holidays, specific days, and seasons. The power consumption patterns in most industrial and commercial sectors are high on weekdays and low on holidays. However, some industrial buildings have irregular power consumption patterns, regardless of season and holiday. Industrial buildings with irregular power consumption cannot accurately predict medium- to long-term power forecasting. Consequently, the utility is unable to manage power supply in response to power demand. Therefore, in this study, two industrial buildings (i.e., companies T and B) with different power consumption patterns were selected to evaluate the medium- and long-term power forecasting performance accuracies of the proposed models. Company B is a two-shift manufacturer and Company T is a three-shift livestock processing firm. The two industrial buildings are located in Naju-si, Jeonnam, South Korea, and their electricity consumption data (per day) are collected monthly. The hourly power usage data comprised three years' worth of data from 2017 to 2019. In addition, we measured the performance of the proposed models by adopting mean absolute error (MAE), root mean squared error (RMSE), coefficient of variation RMSE (CVRMSE), mean absolute error (MAPE), coefficient of determination (R\({}^{2}\)), and computation time. The remainder of this study is organized as follows: Section 2 analyzes the power consumption of two buildings with different power consumption patterns and explains the deep learning techniques, DNN and LSTM. Section 3 describes the multivariate, DNN, and LSTM models proposed herein; Section 4 describes the test environment, including the test data set, and analyzes the test results; and Section 5 concludes the study and discusses future research.