Abstract
Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.
Highlights
With the increasingly serious problem of energy shortage and environmental pollution, energy saving and emission reduction strategy is the key measure to promote green development [1,2], and renewable energy is increasingly favored by people [3]
Screen the original data set through correlation analysis to obtain data indicators with high correlation, which can used as the input data of complementary ensemble empirical mode decomposition (CEEMD)-harmony search (HS)-KELM forecasting model; Decompose the original wind power sequence X through CEEMD method to obtain n sub sequences components from high frequency to low frequency, which are n − 1 intrinsic mode function IMFi (t) and one approximately monotonous residual R(t); Utilize sample entropy (SE) theory to calculate the complexity of each subsequence and reconstruct the subsequence decomposed by CEEMD; HS-KELM model is constructed for each subsequence and the forecast values of each subsequence are obtained; Superimpose the wind power forecasting results of each subsequence to obtain the final forecasting results
Aiming at short-term forecasting of non-linear and unsteady wind power time series, a hybrid forecasting model consisting of CEEMD-SE and HS optimized KELM is proposed in this paper
Summary
With the increasingly serious problem of energy shortage and environmental pollution, energy saving and emission reduction strategy is the key measure to promote green development [1,2], and renewable energy is increasingly favored by people [3]. Yan et al [34] proposed a new hybrid wind speed prediction method, which uses correlation-assisted discrete wavelet transform (DWT) for decomposition the wind speed sequence and utilizes generalized autoregressive conditional heteroscedastic (GARCH) to reflect the fluctuation of the subsequence and adjust the parameters of the prediction model based on LSSVM in real time, so as to reflect the change of wind speed better This method has achieved good results in accuracy and stability. On account of the above analysis, for the purpose of obtaining more accurate wind power forecasting results, this paper proposes a multi-step hybrid wind power forecasting model combining complete ensemble empirical mode decomposition (CEEMD), sample entropy and improved extreme learning machine with kernel (KELM).
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