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

TL;DR

This paper introduces the use of geostrophic wind as a predictor in a space-time statistical model to significantly improve short-term wind speed forecasts, outperforming traditional methods that incorporate pressure and temperature.

Contribution

The study proposes a novel approach of integrating geostrophic wind into the TDD model, enhancing forecast accuracy for 1-6 hour ahead predictions in wind energy applications.

Findings

Achieved 13.9% to 22.4% error reduction over persistence for 2-hour forecasts.

Outperformed previous space-time methods with 5.3% to 8.2% lower errors.

Demonstrated effectiveness using data from West Texas.

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.