Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

Hao Chen,Yubo Tao,Jianzhong Zhang,Fenglei Tan

doi:10.1186/s41601-019-0146-0

Hao Chen, Yubo Tao + Show 2 more

Open Access

https://doi.org/10.1186/s41601-019-0146-0

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Abstract

Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.

Highlights

On account of the lack of fossil resources and environment protection demand, wind power is becoming one of the most rapidly growing renewable energy sources, and regarded as an appealing alternative to conventional power generated from fossil fuel, which plays a very important role in national energy policies all around the world
This paper focuses on the asymmetric characteristics in the volatility of wind power time series, which is very different from the related literature
Since the News Impact Curve (NIC) of standard Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is symmetric, it is often used as a benchmark to illustrate the asymmetry of other GARCH type models

Summary

Introduction

On account of the lack of fossil resources and environment protection demand, wind power is becoming one of the most rapidly growing renewable energy sources, and regarded as an appealing alternative to conventional power generated from fossil fuel, which plays a very important role in national energy policies all around the world. Models used for wind speed forecasting can be usually categorized as physics-based models [5], statistical models, and spatial models [6], which includes ARIMA [7], Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [8], Kalman filters [9] and more recent machine learning technologies such as neural networks [10, 11] and machine learning and deep learning methods [12, 13], are widely used in recent literature papers. This paper focuses on the asymmetric characteristics in the volatility of wind power time series, which is very different from the related literature. The classical NIC has some limitations in the analysis on the wind power time series with complicated volatility characteristics. With the refined NIC with a BSC and ACI, the responses in conditional variance of GARCH-type models are analyzed.

Asymmetric GARCH models

Improved models considering non-Gaussian distribution

The classical NIC

Modeling

Forecasting performance The wind power forecasting formula is given by

Conclusions