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Accurate generation of realistic future scenarios of renewable energygeneration is crucial for long-term planning and operation of electricalsystems, especially considering the increasing focus on sustainable energy andthe growing penetration of renewable generation in energy matrices. Thesepredictions enable power system operators and energy planners to effectivelymanage the variability and intermittency associated with renewable generation,allowing for better grid stability, improved energy management, and enhanceddecision-making processes. In this paper, we propose an innovative method forgenerating long-term hourly scenarios for wind and solar power generation,taking into consideration the correlation between these two energy sources. Toachieve this, we combine the capabilities of a Variational Autoencoder (VAE)with the additional benefits of incorporating the Radial Basis Function (RBF)kernel in our artificial neural network architecture. By incorporating them, weaim to obtain a latent space with improved regularization properties. Toevaluate the effectiveness of our proposed method, we conduct experiments in arepresentative study scenario, utilizing real-world wind and solar powergeneration data from the Brazil system. We compare the scenarios generated byour model with the observed data and with other sets of scenarios produced by aconventional VAE architecture. Our experimental results demonstrate that theproposed method can generate long-term hourly scenarios for wind and solarpower generation that are highly correlated, accurately capturing the temporaland spatial characteristics of these energy sources. Taking advantage of thebenefits of RBF in obtaining a well-regularized latent space, our approachoffers improved accuracy and robustness in generating long-term hourlyscenarios for renewable energy generation.

Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power combining Variational Autoencoders with Radial Basis Function Kernels