Statistical Approach for Wind Speed Forecasting Using Markov Chain Modelling as the Probabilistic Model

Elwan, Ahmed; Habibuddin, Mohd. Hafiz

doi:10.1109/icpea.2019.8818533

Cited by 1 publication

(1 citation statement)

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“…Wind speed models based on AR/MA, Markov chain models and other discrete models [10][11][12] do not allow for the variation of simulation time step simulation. This limitation does not allow the use of these models for the simulation of wind power systems with a high degree of time sampling, which is a necessary condition for selecting the optimal configuration of power-generating equipment and analyzing steady-state and transient processes in RES-based electric power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process

et al. 2021

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The primary task of the design and feasibility study for the use of wind power plants is to predict changes in wind speeds at the site of power system installation. The stochastic nature of the wind and spatio-temporal variability explains the high complexity of this problem, associated with finding the best mathematical modeling which satisfies the best solution for this problem. In the known discrete models based on Markov chains, the autoregressive-moving average does not allow variance in the time step, which does not allow their use for simulation of operating modes of wind turbines and wind energy systems. The article proposes and tests a SDE-based model for generating synthetic wind speed data using the stochastic differential equation of the fractional Ornstein-Uhlenbeck process with periodic function of long-run mean. The model allows generating wind speed trajectories with a given autocorrelation, required statistical distribution and provides the incorporation of daily and seasonal variations. Compared to the standard Ornstein-Uhlenbeck process driven by ordinary Brownian motion, the fractional model used in this study allows one to generate synthetic wind speed trajectories which autocorrelation function decays according to a power law that more closely matches the hourly autocorrelation of actual data. In order to demonstrate the capabilities of this model, a number of simulations were carried out using model parameters estimated from actual observation data of wind speed collected at 518 weather stations located throughout Russia.

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Section: Literature Reviewmentioning

confidence: 99%