Short-Term Wind Speed Forecasting in Germany

TL;DR

This paper introduces two novel models incorporating periodic interactions for short-term wind speed forecasting in Germany, demonstrating improved accuracy up to three hours ahead using ARFIMA-APARCH processes on real data.

Contribution

The paper proposes two new models using Fourier series and generalized trigonometric functions for wind speed prediction, improving short-term forecast accuracy.

Findings

Forecast accuracy improved up to three hours ahead

Models effectively capture periodicity and heteroscedasticity

Real data analysis confirms the models' effectiveness

Abstract

The importance of renewable power production is a set goal in terms of the energy turnaround. Developing short-term wind speed forecasting improvements might increase the profitability of wind power. This article compares two novel approaches to model and predict wind speed. Both approaches incorporate periodic interactions, whereas the first model uses Fourier series to model the periodicity. The second model takes $l_{2,p}$ generalised trigonometric functions into consideration. The aforementioned Fourier series are special types of the p-generalised trigonometrical function and therefore model 1 is nested in model 2. The two models use an ARFIMA-APARCH process to cover the autocorrelation and the heteroscedasticity. A data set which consist of 10 minute data collected at four stations at the German-Polish border from August 2007 to December 2012 is analysed. The most important finding is an enhancement of the forecasting accuracy up to three hours that is directly related to our new short-term forecasting model.