## 3 Implementation  
The ensemble models for electrical load forecasting were implemented using three separate AutoML frameworks: FlaML, AutoML by H2O.ai, and AutoTS--a past winner of the Makridakis Competition [39]. Each framework automated the selection and tuning of the models in a distinct manner, allowing for a comprehensive comparison of their effectiveness. To focus solely on the predictive power of the time-series models and to avoid the complexity introduced by additional parameters, a univariate time series was chosen for this study. This approach ensured that the analysis remained centered on the time-series data itself, without the influence of external variables. For this study, a time series without a concise behavioral pattern was selected to make it harder for the framework to forecast. The next line plot, in Figure 2, presents the selected load profile, which is a dataset of 22 months range in hourly granularity. The first implementation involves the use of AutoTS, a framework specifically designed for time-series forecasting. AutoTS automates the generation and tuning of time-series models and supports various ensemble-based techniques tailored to sequential data. The primary method used in AutoTS involves model averaging or weighted averaging, where the predictions from different time-series models (including ARIMA, machine learning models, and deep learning models like LSTMs) are combined to produce a final forecast. While AutoTS can support stacking, its strength lies in capturing various patterns in time-series data through model selection and averaging. The weights of the base models were fine-tuned to reduce prediction errors, and the model's performance was assessed using the MAPE metric. AutoTS uses backward cross-validation as the default, a method that validates models on consecutive chunks of recent data, making it suitable for time-series data. Figure 1: Flow of information for the final definition of energy mix. In the second implementation, FlaML was employed to automate the generation of multiple base models and optimize their weights. The approach of this framework centered on efficiently searching through various model configurations to quickly identify optimal models with minimal computational cost. Although FlaML is primarily focused on single-model optimization, leveraging the optimization of hyperparameters, it can support basic ensembling methods like bagging (Bootstrap Aggregating) if explicitly configured by the user. However, FlaML does not inherently emphasize complex ensemble strategies. The optimized ensemble model was then evaluated on the testing set using the mean absolute percentage error (MAPE) metric, which provided a clear measure of prediction accuracy. This tool automatically selects a resampling strategy based on the dataset size and time budget, with five-fold cross-validation as the default when cross-validation is selected. The third and final implementation utilizes the AutoML framework by H2O.ai. This tool is known for its robust ensemble-based techniques, particularly its use of stacked ensembles. AutoML automatically trains a range of models, including GLMs, random forests, gradient-boosting machines, and deep learning models, and then creates ensembles using stacking. In this approach, the base models (level 0 models) are combined using a meta-learner (level 1 model), often a generalized linear model (GLM) or a gradient-boosting machine (GBM). This meta-learner is trained to optimally combine the outputs of the base models, thereby improving predictive performance. AutoML may also incorporate bagging and boosting techniques within its workflow. The performance of the optimized model was again evaluated using the MAPE metric on the testing set. By default, this framework automatically decides between a number of folds for the cross-validation on the dataset.