## 1 Introduction  
Accurate estimations of electrical demand stand as an essential component during the optimization of energy trading strategies that, alongside the estimation error, should always be taken into consideration during trading activities. Electrical load datasets typically form time series that display patterns in behavior, leading to variable fluctuations in the data due to seasonal shifts, economic fluctuations, and consumer habits, which add complexity to accurate load forecasting [1, 2]. Conventional forecasting approaches often depend on manual intervention [3, 4], are prone to errors, are time-consuming, and include detailed data pre-processing, a manual selection of features, and expertise in specific domains. However, this method not only costs time but also lacks scalability for large datasets or varied load profiles, particularly in energy portfolios that aggregate multiple time series for the final scheduling to the energy market. Additionally, the reliance on manual processes increases the potential for human error and may limit the ability to quickly adapt to changing market conditions, especially during optimization tasks [5]. Recently, AutoML frameworks have emerged as essential tools for overcoming the obstacles of traditional methods. These frameworks automate model selection, with pre-processing calculations on data, hyperparameter tuning, and feature engineering, significantly reducing the necessity for manual intervention [6]. This research focuses on evaluating the effectiveness of various AutoML frameworks for the prediction of a time series, domain-specific to the energy sector; this is followed by a comparative analysis to estimate their value in the Greek wholesale market using a stochastic optimization objective function for the diversification of the portfolio across different markets like conventional portfolio theory introduces [7, 8]. Regarding the above-mentioned conventional approaches, [PERSON] and [PERSON] [9] explored the application of ARIMA models in time-series forecasting, highlighting their effectiveness in capturing linear patterns in time-series data. However, they also noted that these models often require extensive data pre-processing and a deep understanding of the underlying domain, which can limit their widespread adoption. This is why AutoML technologies have started to attract supporters. As demonstrated by [PERSON] [3], automated algorithms can automate the implementation of a forecast model, significantly reducing the need for domain-specific knowledge and making advanced statistical methods more accessible and efficient for practitioners in energy trading, as this study follows. In a more recent study, [PERSON] [1] examined the use of advanced neural networks, including recurrent neural networks (RNNs), bidirectional long-short term memory (LSTM), and gated recurrent unit (GRU) models for short-term load forecasting. They achieved substantial prediction accuracy, which was reflected in the low mean absolute percentage error (MAPE) values. Other studies also point to using LSTMs in the energy sector [2; 10]. However, the adoption of AutoML--particularly through techniques like Neural Architecture Search (NAS) as discussed by [PERSON] [11]--could further enhance these outcomes by automating the design and optimization of complex neural network architectures. This would reduce the need for manual experimentation and enable more robust and scalable forecasting solutions in large-scale electrical power systems. Additionally, advancements in machine learning, such as transformer-based models, have shown great promise in electrical load forecasting due to their ability to capture complex patterns and long-term dependencies in time-series data [12], although, with the focus on simplicity, pretreatment methods on the dataset were avoided. This is why this study avoids the implementation of complex neural architecture and computationally intensive networks [13] and adopts automated machine learning workflows.