## 1 Introduction  
The prime role of manufacturers is to maintain an equilibrium between energy supply and load consumption, allowing load forecasting as a crucial factor for the electric power industry. The load forecasting duration is considered with three different domains, short-term, medium-term, and long-term, which are used for forecasting a day, a month, or a year. Short-term load forecasting (STLF) focuses on a load of each hour or 30 min daily for years. The electricity availability is improved by making use of appropriate forecasting techniques, resulting a reduction in both generating and operating costs of the electricity industry. Additionally, it decreases costs associated with the performance of short-term scheduling functions and security access of the power system [1].  
Traditional statistical models such as regression analysis [2], moving average [3], exponential smoothing [4], and stochastic time series models [5] are applied for time series forecasting. Moreover, artificial intelligence models including support vector machines [6], artificial neural network (ANN) [7], and fuzzy time series [8] are widely used in many forecasting applications. Even though the neural network is highly significant in artificial models for nonlinear time series problems [9], the efficiency of the neural network is questionable due to the back propagation method during the training process with multiple hidden layers [10]. It also affects the possibility of fast convergence due to longer processing durations. Furthermore, not all inputs and outputs are related to each other in a simple neural network which memorizes continuous data. Therefore, long short-term memory (LSTM), convolutional neural network (CNN), and deep neural network (DNN) are proposed to overcome time series problems and memorize the data with high accuracy. Despite these deep learning (DL) models, ensemble machine learning (ML) methods such as bagging, boosting, and stacking are introduced in order to convert from the above-mentioned weak learners to strong learners, improving the accuracy.  
These ensemble methods basically apply multiple ML algorithms, so-called base models, to produce the optimal forecasting model. The final generated forecasting model could improve the forecasting performance and accuracy of single ML models. Recently, ensemble learning has been widely deployed in many applications, such as in the banking sector, medical data predictions, fraud detection, etc. In this research, the selected two ML algorithms: linear regression (LR) and support vector regression (SVR), are considered the simple linear-based models that have weaknesses during training. During LR training, the model can face problems like underfitting due to not capturing complex data properly, failing to detect outliers, and handling input data wrongly. Similarly, the SVR algorithm is not good at handling large datasets and performing when target classes overlap. As these two simple ML learners train individually and use an averaging process, bagging learning is selected over other ensembles in this paper. Therefore, the primary application of bagging is to achieve less variance than any model has individually in the prediction by producing the additional data, while other ensembles need different combinations, repetitions, and another meta-model in the training process. It can also avoid the overfitting problem and handle higher dimensional data efficiently.