In this paper, we present a modeling and simulation framework for conducting the adequacy assessment of a wind-integrated power system accounting for the associated uncertainties. A multi-perceptron artificial neural network (NN) is trained by a non-dominated sorting genetic algorithm-II (NSGA-II) to forecast point-values and prediction intervals (PIs) of the wind power and load. The output of the assessment is given in terms of point-valued and interval-valued Expected Energy Not Supplied (EENS). We consider different scenarios of wind power and load levels, to explore the influence of the uncertainty in wind and load predictions on the estimation of system adequacy.
