| 基于EVMD和布谷鸟算法的短期风功率区间预测 |
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| 引用本文： | 张亚刚,赵云鹏,王思祺.基于EVMD和布谷鸟算法的短期风功率区间预测[J].太阳能学报,2022,43(8):292-299. |
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| 作者姓名： | 张亚刚 赵云鹏 王思祺 |
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| 作者单位： | 新能源电力系统国家重点实验室（华北电力大学）,北京 102206 |
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| 基金项目： | 国家自然科学基金重点项目（51637005）; 河北省自然科学基金（G2020502001） |
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| 摘 要： | 为提高短期风功率预测精度和预测的可控性,提出一种基于能量差优化变分模态分解和布谷鸟优化组合神经网络的短期风功率预测模型。采用能量差优化变分模态分解（EVMD）的模态数,将EVMD用于短期风功率分解,基于EVMD分解序列的不同模态特点,对非线性序列采用布谷鸟优化反向传播神经网络（CS-BPNN）,对平稳序列采用自回归滑动平均模型（ARMA）,并重构加权得到点预测值,并基于EVMD分解所丢失的序列信息构建核密度估计,在点预测模型的基础上,进行风功率的区间预测。将所提预测方法用于澳大利亚风电场的实际算例,实验结果表明,该方法可提高短期风功率预测的准确性。
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| 关 键 词： | 风功率 预测 信号处理 EVMD 神经网络 布谷鸟算法 |
| 收稿时间： | 2020-12-10 |
SHORT TERM WIND POWER INTERVAL PREDICTION BASED ON EVMD AND CUCKOO ALGORITHM |
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| Zhang Yagang,Zhao Yunpeng,Wang Siqi.SHORT TERM WIND POWER INTERVAL PREDICTION BASED ON EVMD AND CUCKOO ALGORITHM[J].Acta Energiae Solaris Sinica,2022,43(8):292-299. |
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| Authors: | Zhang Yagang Zhao Yunpeng Wang Siqi |
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| Affiliation: | State Key Laboratory of New Energy Power System（North China Electric Power University）, Beijing 102206, China |
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| Abstract: | In order to improve the accuracy and controllability of short-term wind power prediction, a short-term wind power prediction model based on energy difference optimal variational mode decomposition and cuckoo optimal combination neural network is proposed. The energy difference is used to optimize the number of modes of the variational modal decomposition （EVMD）, and EVMD is used for short-term wind power decomposition. Based on the different modal characteristics of the EVMD decomposition sequence, a cuckoo optimized back propagation neural network is used for the nonlinear sequence （CS-BPNN）. The autoregressive moving average model （ARMA） is used for the stationary series, and the weighted point prediction value is reconstructed. The kernel density estimation is constructed based on the sequence information lost by EVMD decomposition. Based on the point prediction model, the interval prediction of wind power is carried out. The proposed prediction method is applied to a practical example of wind farms in Australia. The experimental results show that the method can improve the accuracy of short-term wind power prediction. |
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| Keywords: | wind power forecasting signal processing EVMD neural network cuckoo algorithm |
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