System and method for performing wind forecasting

Abstract

A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approachleverages a physical framework based on the unsteady dynamics of earth's atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.

Description

[0001] Statement of Government Interest [0002] This invention was made with government support under Grant No. AGS-1026636 awarded by the National Science Foundation (NSF). The government has certain rights in this invention. [0003] Cross References to Related Applications [0004] This application claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 62 / 139,083, filed March 27, 2015, which is hereby incorporated by reference in its entirety. technical field [0005] The present invention relates generally to the fields of weather forecasting and forecasting for energy supply management. More specifically, the present invention provides systems and methods for performing wind forecasting that more easily and accurately generate short-term wind forecasts that can be used to facilitate energy supply management and other business application. Background technique [0006] Currently, various systems and methods exist for weathe...

AI Technical Summary

Problems solved by technology

However, these short-term forecasts still suffer from large errors, and since most of these data-driven methods do not have any physical anchor points, these short-term forecasts generally outperform naive persistent models at best (around 5-20% improvement)

This limits the usefulness of such wind forecasts in the energy industry as well as in other contexts

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