Data complexity based hybrid approach to select a model for wind speed forecasting

Abstract

ABSTRACT Recent wind speed forecasting works have applied various time series decomposition methods to segregate a complex wind time series into linear and nonlinear components. Then, existing statistical techniques are used to model each component separately. This approach is reliable and produces accurate results when applied to complex wind time series. However, forecasting the stationary wind time series may produce unsatisfactory results. In order to address this problem, we propose a hybrid approach for wind speed forecasting. In this approach, seven different data complexity measures are used to calculate time series data complexity. Then, the measured complexity is used by the proposed model to decide whether to go with the decomposition technique or not. This paper has suggested Empirical Mode Decomposition for decomposition, which has achieved an incredible surge in a large number of data streams. Similarly, two leading statistical forecasting approaches, i.e., Autoregressive Integrated Moving Average and Generalized Autoregressive Score, are employed to model linear time series and decomposed components of the complex nonlinear time series. For comparative analysis, Mean Absolute Error, Root Mean Squared Error, and R-square are used as evaluation metrics. Experimental result in this work suggests that the proposed hybrid approach performs equivalently well for complex time series and achieves considerable improvement for stationary wind time series. The percentage improvement is 95.29% in the case of ARIMA and 49.09% when GAS is applied.