The common practice to calculate wind generation capacity values relies more on heuristic approximations than true system estimations. In this paper we proposed a more accurate method. In the first part of our analysis, a Monte Carlo simulation was created based on Markov chains to provide an...

There are always two major sources of uncertainty in measurements related to lifetime surveys: variation among the observations and imprecision of individual observation called fuzziness. The typical statistical analysis is based on variation among the observations and does not consider the...

A Markov-switching model in wind speed forecasting is examined in this research. The proposed method employs a regime switching process governed by a discrete-state Markov chain to model the nonlinear evolvement of the wind speed time-series. A Bayesian inference rather than the traditional...

The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models...

This paper considers systems whose input signals are fuzzy stochastic processes of second order. The analysis is entirely restricted to discrete time linear time-invariant systems. Convergence conditions of the output are given. The equations on the mean value functions and the covariance...