Space-Time Statistics for Wind Power Forecasting

Project: Research project

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.

StatusFinished
Effective start/end date7/15/106/30/14

Funding

  • National Science Foundation: $180,000.00

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