  Wind farms can be regarded as complex systems that are, on the one hand,
coupled to the nonlinear, stochastic characteristics of weather and, on the
other hand, strongly influenced by supervisory control mechanisms. One crucial
problem in this context today is the predictability of wind energy as an
intermittent renewable resource with additional non-stationary nature. In this
context, we analyze the power time series measured in an offshore wind farm for
a total period of one year with a time resolution of 10 min. Applying detrended
fluctuation analysis, we characterize the autocorrelation of power time series
and find a Hurst exponent in the persistent regime with cross-over behavior. To
enrich the modeling perspective of complex large wind energy systems, we
develop a stochastic reduced-form model ofpower time series. The observed
transitions between two dominating power generation phases are reflected by a
bistable deterministic component, while correlated stochastic fluctuations
account for the identified persistence. The model succeeds to qualitatively
reproduce several empirical characteristics such as the autocorrelation
function and the bimodal probability density function.
