## 1 Introduction
Building on these advances, [PERSON] introduced a hybrid approach that combined statistical methods with machine learning techniques [14]. Their model utilized a new weighted multi-objective particle swarm optimization algorithm to optimize short-term load forecasting, effectively leveraging the strengths of both statistical and machine-learning methods. Automated procedures--particularly through the Sequential Model-Based Optimization (SMBO) that stands as an automated implementation methodology, as outlined by [PERSON] [15]--can address these challenges by automating the integration and optimization of diverse modeling techniques, simplifying the deployment of sophisticated hybrid models, and enhancing computational efficiency. The relevance and impact of AutoML in the context of forecasting are further supported, with accurate and robust implementations [16]. The aforementioned studies underscore the evolving complexity of forecasting methods on time series and also illustrate the critical role of AutoML, which could stand as crucial regarding the simplicity and robustness of these frameworks and their underlying methodologies. By automating model selection, tuning, and integration, AutoML not only mitigates the limitations of traditional approaches but also accelerates the adoption of advanced forecasting techniques, driving more efficient and accurate decision-making in the energy trading sector. It achieves this by using the forecasts for the allocation of an optimal energy mix across different markets under stochasticity due to the fact that in the specific case of load portfolios, there are always unpredictable factors for future allocations. Beyond the forecasting procedures, there are also other factors of importance for the optimal aggregation to the energy market. The first consideration is the lowest possible cost of energy for a given time horizon, and the second stands as the risk involved for the above-mentioned allocated price. Different studies [17; 18] depicted the conjunction of stochastic optimization problems in the energy sector. Additionally, inspired by risk quantification practices from the traditional finance sector, it is crucial to consider the correlation among different markets for the same product. This interdependence should be accounted for when analyzing and forecasting market trends and behaviors [19]. Additionally, other studies have emphasized the significance of forecasting in high-frequency markets, underscoring the critical role of automated prediction techniques in enhancing accuracy and decision-making [20]. The purpose of this paper is to create an industry-viable approach to forecasting as well as the conjunction of these forecasts with an energy procurement optimization framework, which combines different energy markets and involves the financial risks of these markets. As depicted by [PERSON] [21], it is viable to approach the energy management of a microgrid and beyond that under a portfolio entity as this manuscript proposes, so as to manage risks and be able to receive decisions under a strategic way of energy procurement. Therefore, it addresses the existing scientific gap of by introducing an agnostic way to integrate the forecast error into the energy trading strategy until the optimal energy mix, as far as the cost and risk of the current market state, is defined. The shortfall costs of the energy are taken into account for each time period, which is the imbalance between the acquired energy and the real consumption. The rest of this manuscript is organized as follows: Section 2 presents the methodologies, Section 3 the implementation of AutoML frameworks, and finally, Section 4 illustrates two simplified scenarios of the implementation of the framework as well as a comparison with a standard contract located in the Greek energy market.