The Annals of Applied Statistics

Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting

Xinxin Zhu, Kenneth P. Bowman, and Marc G. Genton

Full-text: Open access

Abstract

Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space–time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth’s rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1- to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space–time methods in this setting.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1782-1799.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091234

Digital Object Identifier
doi:10.1214/14-AOAS756

Mathematical Reviews number (MathSciNet)
MR3271353

Zentralblatt MATH identifier
1304.62142

Keywords
Geostrophic wind space–time statistical model wind energy wind speed forecasting

Citation

Zhu, Xinxin; Bowman, Kenneth P.; Genton, Marc G. Incorporating geostrophic wind information for improved space–time short-term wind speed forecasting. Ann. Appl. Stat. 8 (2014), no. 3, 1782--1799. doi:10.1214/14-AOAS756. https://projecteuclid.org/euclid.aoas/1414091234


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