Abstract
Many of the previous investigations predicted wind speed by directly using wind speed data, which rarely considered physical characteristics of wind speed and was difficult to improve prediction accuracy further. Therefore, a novel self-adaptive wind speed prediction model considering atmospheric motion and fractal feature is developed in this paper. Lorenz-Stenflo (LS) equation is employed to describe the disturbances and chaos effect caused by atmospheric motion on wind speed. One-dimension LS motion series obtained by LS equation is adopted to improve the decomposition effect of wind speed by ensemble empirical mode decomposition (EEMD). The fractal feature of wind speed series is primitively adopted to determine the key parameter in LS equation. Then back propagation (BP) neural network model optimized by genetic algorithm (GA), as a fundamental prediction model, is used for prediction. Eight groups of wind speed series on different time scales from two wind farms are tested and evaluated. The proposed model effectively overcomes the disturbances of atmospheric motion and achieves promising prediction accuracy. Meanwhile, the criterion based on fractal feature ensures accurate selection of the key parameter in atmospheric motion equation according to different features of sampled wind data.
Highlights
With the depletion of traditional fossil fuels and increasingly serious environmental problems, wind energy has been highly valued by countries in the world [1], [2]
V (m/s) model gets the least deviations between prediction values and actual values. This is because Fractal Dimension (FD)-LS-ensemble empirical mode decomposition (EEMD) can adaptively select the one-dimension LS motion series dLS, which can effectively improve the decomposition process of wind speed by EEMD, weaken the disturbances and chaos effect caused by atmospheric motion on wind speed and enhance the smooth degree of intrinsic mode functions (IMFs)
The wind speed prediction model based on FD-LS-EEMD and GABP neural network was proposed in this paper
Summary
With the depletion of traditional fossil fuels and increasingly serious environmental problems, wind energy has been highly valued by countries in the world [1], [2]. Statistical models establish the functional model among time series data based on recursive theory [10] They can capture the relationship according to historical data and are suitable for short term wind speed prediction. They are restricted in application for non-stationary time series. The literature [27] proposed that chaos characteristic is existed in short time wind speed time series, so the prediction accuracy of wind speed prediction model considering the chaos characteristics has been improved.
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