Representation of correlations between renewable energy sources in state-space sampling using Kernel Density Estimation and Bayesian Networks

This paper explores a method for preserving correlations between random variables in the state-space sampling technique that is adopted in Non-Sequential Monte Carlo Simulation. The proposed method combines the Kernel Density Estimation technique with Bayesian Networks to encode dependency relationships between variables. The model involves transforming historical input data through normalization and aggregation. The algorithm was evaluated using actual renewable generation (wind and solar) and load time series for the Northeast region of Brazil. The original and sampled series were then compared using scatter plots and correlation metrics to validate the effectiveness of the method.