An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction
IEEE Transactions on Neural Networks and Learning Systems
We consider the task of performing prediction with neural networks on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multi-layer perceptron neural network (NN) is trained to map interval-valued input data into interval outputs, representing the prediction intervals (PIs) of the real target values. The NN training is performed by non-dominated sorting
... ated sorting genetic algorithm-II (NSGA-II), so that the PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). Demonstration of the proposed method is given on two case studies: (i) a synthetic case study, in which the data have been generated with a 5-min time frequency from an Auto-Regressive Moving Average (ARMA) model with either Gaussian or Chi-squared innovation distribution; (ii) a real case study, in which experimental data consist in wind speed measurements with a time-step of 1-hour. Comparisons are given with a crisp (single-valued) approach. The results show that the crisp approach is less reliable than the interval-valued input approach in terms of capturing the variability in input. Index Terms-Interval-valued neural networks, multiobjective genetic-algorithm, prediction intervals, short-term wind speed forecasting, uncertainty. Enrico Zio (M,SM) received the Ph.D. degree in nuclear engineering from Politecnico di Milano and MIT in 1995 and 1998, respectively. He is currently Director of the Chair on Systems Science and the Energetic Challenge, European Foundation for New Energy-Électricité de France (EDF), at ÉcoleCentrale Paris (ECP) and ÉcoleSuperieured'Électricité (SUPELEC) and full professor at Politecnico di Milano. His research focuses on the characterization and modeling of the failure/repair/maintenance behavior of components, complex systems and their reliability, maintainability, prognostics, safety, vulnerability and security, Monte Carlo simulation methods, soft computing techniques, and optimization heuristics.