A Genetic Algorithm and Neural Network Technique for Predicting Wind Power under Uncertainty

Wind speed uncertainty, and the variability in the physical and operating characteristics of turbines have a significant impact on power system operations such as regulation, load following, balancing, unit commitment and scheduling. In this study, we consider historical values of wind power for predicting future values taking into account both the variability in the input and the uncertainty in the model structure. Uncertainty in the hourly wind power input is presented as intervals of within-hour variability. A Neural Network (NN) is trained on the interval-valued inputs to provide prediction intervals (PIs) in output. A multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) is used to train the NN. A multi-objective framework is adopted to find PIs which are optimal for accuracy (coverage probability) and efficacy (width).