## 1 Introduction
### Prior Works  
In recent times, ML models have been widely used in the field of forecasting to solve nonlinear complex problems which could not be solved by traditional time series models. Among them, the LR model is one of the simple and popular algorithms based on supervised learning for regression tasks. This model depends on independent variables for the target predictions and carries out the relationship between variables and forecasting [2]. The SVR algorithm is another useful algorithm for supervised regression tasks. The reasonable forecasting results are achieved at the cost of much higher computation time, which is caused by training gradient descent for updating the parameters and reducing loss function [11]. Moreover, most ML models cannot handle large data in the training network. To handle the weakness of ML models, DL models were introduced at the beginning of the 20 th century and have successfully been applied since then. They have demonstrated strengths in handling complex non-linear relationships, model complexity, and computational efficiency [10]. One of the popular DL models is the deep neural network (DNN) model consisting of a large number of processing layers. It is a class of neural network models that comprises an input layer, an output layer, and a large number of hidden layers. Unlike the simple ANN model, the DNN model can handle more than two hidden layers and have a better backpropagation algorithm for the back forward training process. This algorithm uses stochastic gradient descent over simple gradient descent to overcome speed convergence obstacles and avoid local minima [12]. Nevertheless, the weakness of DNN cannot memorize the sequential data and there is no pre-training process in it. Typically, a deep belief network (DBN) model is based on ANN with multiple hidden layers, which can reduce the approximation error by adding more hidden layers in between the input and output layers. This architecture is based on its performance in related studies [12]. Deep architectures are required to detect higher-level representation and capture prominent abstractions in the network. DBN has become an efficient model in the fields of regression, image classification, and automatic speech, face recognition, natural language processing, and bioinformatics, etc. Ref. [13]. In the article of [14], the DBN application was implemented and developed for modeling the generator bearing temperature and performed more accurate predictions than SVR, ANN, and extreme learning machine (ELM) in generator bearing failures for wind turbines. Moreover, the deep architecture includes LSTM [15], recursive neural network (RNN), CNN [16], and so on. Each model uses computational methods which are trained with multiple hidden layers, to learn representations of data with numerous abstraction levels. These DL modelscan detect complex structures in large data sets by using the backpropagation process, therefore, solving the drawbacks from the ML technique. Various researchers have investigated different approches, such as combining two or more forecasting models regarding the performance efficiency. Recently, the ensemble methods have become popular to convert weak ML learners to strong learners to overcome the weakness in ML algorithms. In the cited work [17], five ML models were combined using the ensemble method to lessen forecast errors. In addition to the selected models, some studies based on the feedforward multilayer perceptron using supervised learning algorithms have been conducted [18]. DBN was successfully applied to forecast load demand with hourly electricity consumption data in Macedonia [19]. Moreover, the combination of classification and regression tree (CART) and DBN models was proposed to improve forecasting accuracy by classifying load data [20]. [PERSON] et al.