  Wind is one kind of high-efficient, environmentally-friendly and
cost-effective energy source. Wind power, as one of the largest renewable
energy in the world, has been playing a more and more important role in
supplying electricity. Though growing dramatically in recent years, the amount
of generated wind power can be directly or latently affected by multiple
uncertain factors, such as wind speed, wind direction, temperatures, etc. More
importantly, there exist very complicated dependencies of the generated power
on the latent composition of these multiple time-evolving variables, which are
always ignored by existing works and thus largely hinder the prediction
performances. To this end, we propose DEWP, a novel Deep Expansion learning for
Wind Power forecasting framework to carefully model the complicated
dependencies with adequate expressiveness. DEWP starts with a stack-by-stack
architecture, where each stack is composed of (i) a variable expansion block
that makes use of convolutional layers to capture dependencies among multiple
variables; (ii) a time expansion block that applies Fourier series and
backcast/forecast mechanism to learn temporal dependencies in sequential
patterns. These two tailored blocks expand raw inputs into different latent
feature spaces which can model different levels of dependencies of
time-evolving sequential data. Moreover, we propose an inference block
corresponding for each stack, which applies multi-head self-attentions to
acquire attentive features and maps expanded latent representations into
generated wind power. In addition, to make DEWP more expressive in handling
deep neural architectures, we adapt doubly residue learning to process
stack-by-stack outputs. Finally, we present extensive experiments in the
real-world wind power forecasting application on two datasets from two
different turbines to demonstrate the effectiveness of our approach.
