  Renewable energy is essential for energy security and global warming
mitigation. However, power generation from renewable energy sources is
uncertain due to volatile weather conditions and complex equipment operations.
To improve equipment's operation efficiency, it is important to understand and
characterize the uncertainty in renewable power generation. In this paper, we
proposed a conditional kernel density estimation method to model the
distribution of equipment's power output given any weather conditions. It
explicitly accounts for the temporal dependence in the data stream and uses an
iterative procedure to reduce the bias in kernel density estimation. Compared
with existing literature, our approach is especially useful for the purposes of
equipment condition monitoring or short-term renewable energy forecasting,
where the data dependence plays a more significant role. We demonstrate our
method and compare it with alternatives through real applications.
