# Forecasting of Power Demands Using Deep Learning
## 1 Introduction  
The commercial electric power companies struggle to provide end-users with stable and safe electricity. Therefore, designing efficient forecasting models is a vital step for the planning of the operation of electronic power systems. The demand patterns of electricity could be affected by various factors such as time, economy, social, and environmental factors [1, 2]. The power forecasting models can be classified based on their predictive ability into short-term, medium-term, and long-term. Short-term power forecasting models predict up to 1 day/week ahead and used for scheduling the generation and transmission of electricity, medium-term power forecasting models predict for 1 day/week to 1 year ahead and used for fuel preparation, long-term power forecasting models predict more than 1 year and used for developing power supply and delivery system [3, 4, 5]. Power demands prediction frameworks can be divided into statistical models, grey models, and artificial intelligence models [6]. In statistical models, the correlation between the inputs and the outputs is statistically figured out using empirical models such as log-linear regression models, co-integration analysis and autoregressive integrated moving average (ARIMA), combined bootstrap aggregation (bagging), and exponential smoothing. In the grey models, the researchers integrated a partial theoretical structure with empirical data to build the structure and as result, a limited amount of data are required to infer the behavior of the electrical systems. In artificial intelligence models, they learn to model complex relationships between the outputs and inputs based on the available training data. Different techniques have been applied in electricity demand forecasting including time series models [7], bolt-winters and seasonal regression [8], multiple linear regression [9; 10], first-order fuzzy time series [11], autoregressive integrated moving average (ARIMA) [12], seasonal ARIMA (SARIMA) [2], support vector machine (SVM) [13], support vector regression [14], Least square SVM (LSSVM) [1], and artificial neural network (ANN) [10; 14; 15]. For example, the work conducted by [11] used the monthly demand data from 1970 to 2009 as a training dataset and 2010 data as a testing dataset. The electricity demand in Thailand was predicted by including gross domestic product, maximum ambient temperature, and the population. These features were used as input to a neural network [10]. The authors of [14] used Bayesian regularization in the autoregressive neural network for electricity demand forecasting. Authors in [16] compared different forecasting methods such as neural networks, fuzzy logic, and autoregressive process, and as a result, they found that neural networks and fuzzy logic are more accurate that autoregressive processes. Different techniques were combined by the authors of [17] in which they combined the support vector machine, neural networks, autoregressive integrated moving average, and generalized regression neural network. The authors of [18] proposed a traditional feed-forward neural network with one output node that can predict peak load in one hour or one day in the future. Also, the researchers explored radial basis function networks [19], recurrent neural networks [20], and self-organizing maps [21]. All of these traditional methods have a limited performance as they require hand crafted features. Therefore, with the advancement of deep learning, researchers have utilized different deep learning architectures for several electricity demand forecasting problems [22; 23; 24; 25; 26; 27]. Different resolutions have been studied from multiple times a day such as monthly [22], weekly [27], daily [24; 25; 26; 27], every two hours [25], hourly [27], half-hourly [23; 24; 25], minute-by-minute [27]. These models studied different targets such as business consumer [23; 25], household consumer [24; 27], other than business or household consumer [22], and region/country such as UT Chandigarh, India, in [26].