Abstract
Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined; the model is based on cross correlation (CC) analysis and a support vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.
Highlights
In recent decades, increasing attention has been paid to renewable energy around the world due to the limited reserves of nonrenewable resources and the emerging crisis of global climate warming resulting from large amounts of greenhouse gases emission generated by fossil fuel combustion [1, 2]
Because wind power is proportional to the cube of wind speeds and a 10% deviation of the expected wind speed results in an approximately 30% deviation in the expected wind power production [6], the prediction error of wind energy largely depends on the accuracy of wind speed forecasts
Li et al proposed a hybrid model consisting of the artificial neural networks (ANN) and Bayesian approaches, and the results indicated that the hybrid approaches produced forecasting errors that were always smaller than those produced by ANN [23]
Summary
In recent decades, increasing attention has been paid to renewable energy around the world due to the limited reserves of nonrenewable resources and the emerging crisis of global climate warming resulting from large amounts of greenhouse gases emission generated by fossil fuel combustion [1, 2]. Precise short-term wind speed forecasts can minimize scheduling errors that can exert a large impact on grid reliability and marketbased ancillary costs [11] According to these approaches, wind speed prediction can be clustered into two main categories, that is, physical methods and statistical methods [12]. Based on the aforementioned research, predictive models with different relative weaknesses and strengths have been widely studied and developed Among these models, the potential for applying the combined approaches over a much wider application area has a special significance because individual models perform well only under specific and corresponding conditions and may require the use of different models [12]. The proposed hybrid models were applied for wind speeds prediction 1or 3-step ahead based on averaged hourly and 10 min wind speed series, respectively.
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