## 2 Background  
Traditional machine learning techniques used for forecasting can be categorized into three main groups: (1) time series analysis, (2) regression-based approaches, and (3) supervised and unsupervised methods [13]. Time series analysis methods are the most widely used, encompassing techniques like autoregressive integrated moving average (ARIMA) and Holt Winter Exponential Smoothing (HW). These methods, particularly in the context of retail demand forecasting, are highly regarded for their ability to capture trends and seasonal demand patterns [14; 15]. Regression-based methods have the flexibility to consider both independent and dependent variables [5]. And third, supervised and unsupervised models, like artificial neural networks (ANNs) or long short-term memory (LSTM), have been shown to perform better in nonlinear data [16]. The utilization of advanced models in forecasting has experienced growth in various industries, including oil, food and agriculture, public transportation, and retail. The performance of these models has been investigated in these industries, and it was found that ensemble models could outperform other models regarding accuracy [17; 18; 19]. To gain a deeper understanding of the application of advanced algorithms in demand forecasting, we have conducted a search using the keywords \"demand forecasting\" and \"ensemble\" in online databases focusing on recent years. We found a significant number of studies that have used advanced machine learning approaches in the field of energy. For instance, [PERSON] et al. (2016) proposed a new method for predicting crude oil prices using an ensemble empirical mode decomposition (EEMD) and extended extreme learning machine (EELM) to forecast electricity load [20]. [PERSON] et al. (2019) have presented a framework for short-term load forecasting using the Wavenet ensemble [21]. The framework involves transforming the data, determining an optimal time window, and selecting features. The proposed framework outperforms existing similar forecasting techniques, like multilayer perceptron neural networks. In electricity price forecasting, [PERSON] et al. (2022) introduced a hybrid deep neural network approach, which utilizes the Catboost algorithm for feature selection and a bidirectional long short-term memory neural network (BDLSTM) as the main forecasting engine [22]. In a recent study, [PERSON] et al. (2021) proposed a new method for short-term prediction in microgrids called the Ensemble Prediction Network (EPN). The EPN comprises an ensemble of nine linear predictive nodes and is designed to provide an optimal estimate of predicted demand through least-squares optimization under certain constraints [23]. To overcome uncertainty, [PERSON] et al. (2017) used combination approaches in a HAR model that considers lags of realized volatility and other potential predictors [24]. In the tourism industry, [PERSON] (2016) developed and implemented ensemble learners for tourism demand forecasting based on M5P and M5-Rule model trees and random forest algorithms. The learners were evaluated using bagging, boosting, randomization, stacking, and voting techniques for forecasting tourism demand in Turkey [17]. Ensemble models have also been applied in public transportation, where [PERSON] et al. (2018) presented a data-driven framework for short-term metro passenger flow prediction that utilizes both spatial and temporal information. The passenger flow information is obtained from smart-card data, and passenger flow patterns are explored. The proposed framework consists of two basic prediction models and a probabilistic model selection method (random forest classification) to combine the outputs for better prediction [25]. In an agricultural application field, [PERSON] and [PERSON] (2020) investigated the accuracy of forecasting agricultural commodity prices through regression ensembles.