The increased participation of variable renewable energy sources (VRES) in Brazil s electricity matrix brings several challenges to the planning and operation of the Brazilian Power System (BPS), due to the VRES stochasticity. Such challenges involve the modeling and simulation of intermittent generation processes and, in this context, a considerable amount of research has been directed to the theme. In this context, a topic of increasing importance in the literature is related to the development of methodologies for joint stochastic simulation of intermittent resources with complementary characteristics, such as wind and solar sources. Aiming to contribute to this theme, this work proposes improvements in a simulation model already established in the literature, evaluating its applicability based on Brazilian Northeast data. The proposed methodology is based on the discretization of energy time series applying the kmeans machine learning technique, construction of state transition matrices based on the identified clusters, and Monte Carlo simulation to obtain the scenarios. The synthetic series obtained are compared to the results generated by the model already established in the literature from statistical techniques. Regarding the scope of the research objectives, the proposed modeling demonstrated more promising results, generating scenarios that satisfactorily reproduced all the evaluated characteristics of the historical data.