  Power curve is widely used in the wind industry to estimate power output for
planning and operational purposes. Existing methods for power curve estimation
have three main limitations: (i) they mostly rely on wind speed as the sole
input, thus ignoring the secondary, yet possibly significant effects of other
environmental factors, (ii) they largely overlook the complex marine
environment in which offshore turbines operate, potentially compromising their
value in offshore wind energy applications, and (ii) they solely focus on the
first-order properties of wind power, with little (or null) information about
the variation around the mean behavior, which is important for ensuring
reliable grid integration, asset health monitoring, and energy storage, among
others. This study investigates the impact of several wind- and wave-related
factors on offshore wind power variability, with the ultimate goal of
accurately predicting its first two moments. Our approach couples OpenFAST with
Gaussian Process (GP) regression to reveal the underlying relationships
governing offshore weather-to-power conversion. We first find that a
multi-input power curve which captures the combined impact of wind speed,
direction, and air density, can provide double-digit improvements relative to
univariate methods which rely on wind speed as the sole explanatory variable
(e.g. the standard method of bins). Wave-related variables are found not
important for predicting the average power output, but interestingly, appear to
be extremely relevant in describing the fluctuation of the offshore power
around its mean. Tested on real-world data collected at the New York/New Jersey
bight, our proposed multi-input models demonstrate a high explanatory power in
predicting the first two moments of offshore wind generation, testifying their
potential value to the offshore wind industry.
