电力建设2024，Vol.45Issue(8)：97-105,9.DOI:10.12204/j.issn.1000-7229.2024.08.009
基于CEEMDAN和DBO-GRNN的风电功率超短期预测
Ultra-Short-Term Prediction of Wind Power Based on CEEMDAN and DBO-GRNN
摘要
Abstract
To address the problem of inaccurate wind power prediction caused by the excessive volatility of wind power data,this paper proposes a generalized regression neural network(GRNN)method based on the optimization of complementary ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and dung beetle optimizer(DBO).A combination of GRNN and DBO optimization is used for ultra-short-term wind power prediction.First,the original wind power sequence is subjected to time-lag characteristic analysis,and the time series with a strong correlation with the predicted moments is selected for multiplexed time-series modeling.Subsequently,the time series with strong time series are subjected to CEEMDAN decomposition,and a set of intrinsic mode functions(IMFs)and a residual term are obtained.Second,the two sets of the above components are inputted into the GRNN network optimized by the DBO algorithm for the prediction of the components.Subsequently,the prediction components are superimposed to obtain the final prediction result.Example analysis shows that the CEEMDAN-DBO-GRNN prediction model proposed in this paper has higher prediction accuracy,and CEEMDAN can reduce the influence of wind power volatility and randomness on the prediction results.The prediction of the hyperparameter model optimized by the DBO algorithm improves the accuracy of the ultra-short-term wind power prediction to a certain extent.关键词
自适应噪声完备集合经验模态分解(CEEMDAN)/蜣螂优化算法(DBO)/广义回归神经网络(GRNN)/超短期风电功率预测Key words
complementary ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/dung beetle optimization algorithm(DBO)/generalized regression neural network(GRNN)/ultrashort-term wind power forecasting分类
信息技术与安全科学引用本文复制引用
刘洋,伍双喜,朱誉,杨苹,孙涛..基于CEEMDAN和DBO-GRNN的风电功率超短期预测[J].电力建设,2024,45(8):97-105,9.基金项目
This work is supported by Guangdong Province Key Area R&D Program Funding(No.2021B0101230003)and China Southern Power Grid Corporation Science and Technology Project Grant(No.GDKJXM20220335). 广东省重点领域研发计划资助项目(2021B0101230003) （No.2021B0101230003）
南方电网公司科技项目资助(GDKJXM20220335) （GDKJXM20220335）
