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...

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...

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 binary states, i.e., success or failed state assumptions used in conventional reliability are inappropriate for reliability analysis of complex industrial systems due to lack of sufficient probabilistic information. For large complex systems, the uncertainty of each individual parameter...