Project Details
Description
The technology to harvest electricity from wind energy
is now sufficiently advanced to make entire cities powered
by wind a reality. High-quality, short-term forecasts of
wind speed are vital for making this a more reliable energy
source. The investigator proposes to study two topics in
wind power forecasting, develop relevant methodologies and
analyze their corresponding properties and performances both
theoretically and through Monte Carlo simulations studies.
The new methods will be applied to a first-of-a-kind testbed
wind dataset from northeastern US. The first topic concerns
shrinkage and selection of space-time variables in various
models for wind power forecasting. The investigator proposes
to combine techniques such as partial least squares and the
adaptive lasso with space-time correlation information that
naturally arises among the predictor variables in the wind
forecasting models. The second topic concerns realistic
forecast evaluations in the context of wind power forecasting.
The investigator proposes to develop new loss functions that
are economically relevant for wind power and study their
properties and their use in building and evaluating forecasting
models in the wind energy arena.
The primary impact of this project is that the new methods
for wind power forecasting will provide valuable tools to
applied practitioners in wind farming. There is an obvious
need for statisticians to be involved in such important
problems related to renewable and clean energies for the
well-being of our society. In particular, the research
topics address crucial and major challenges for advancing
the use of energy from wind.
Status | Finished |
---|---|
Effective start/end date | 7/15/10 → 6/30/14 |
Funding
- National Science Foundation: $180,000.00