基于iceemdan的联合风电预测

Q3 Engineering
ZhenJun Wu, Yuan Dong, Ping He
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Objective:: In order to reduce the volatility and randomness of wind power series and increase the accuracy of wind power prediction, a wind power combination model based on the ICEEMDAN (improved adaptive noise full set empirical mode decomposition) method is proposed. objective: In order to reduce the volatility and randomness of wind power series and increase the accuracy of wind power prediction, a wind power combination model based on ICEEMDAN (improved adaptive noise full set empirical mode decomposition) method is proposed Methodology:: First, the complex original wind power data have been decomposed into several relatively simple subsequences using the ICEEMDAN method. Aiming at the different lengths of coarse grain time series and data loss in traditional multi-scale entropy, a fine composite multiscale dispersion entropy is proposed to calculate the entropy value of each decomposition component, and divide the high- and low-frequency modal components to predict the modal components of different frequencies; secondly, differential moving autoregressive model (ARIMA) and shortterm memory neural network (LSTM) are used to establish the prediction models of high- and low-frequency components, respectively. Results:: Finally, the prediction results of each component have been superimposed and reconstructed to obtain the final prediction results. The effectiveness of the combined model is verified by the actual operation data of a European wind farm. result: The effectiveness of the combined model is verified by the actual operation data of a European wind farm. 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引用次数: 0

摘要

背景:随着化石能源的枯竭和环境污染的日益严重,发展可再生能源的任务迫在眉睫。风能作为一种绿色、无污染的可再生能源,在电网中的渗透率不断上升。目的:为了降低风电功率序列的波动性和随机性,提高风电功率预测的准确性,提出了一种基于改进自适应噪声全集经验模态分解(ICEEMDAN)方法的风电组合模型。目的:为了降低风电功率序列的波动性和随机性,提高风电功率预测的准确性,提出了一种基于改进自适应噪声全集经验模态分解(ICEEMDAN)方法的风电功率组合模型。方法:首先,利用ICEEMDAN方法将复杂的原始风电数据分解为多个相对简单的子序列。针对传统多尺度熵中粗粒时间序列长度不同和数据丢失的问题,提出了一种精细复合多尺度色散熵,计算各分解分量的熵值,并对高、低频模态分量进行划分,预测不同频率的模态分量;其次,利用微分运动自回归模型(ARIMA)和短时记忆神经网络(LSTM)分别建立了高频和低频分量的预测模型;结果:最后对各分量的预测结果进行叠加重构,得到最终的预测结果。通过欧洲某风电场的实际运行数据验证了该组合模型的有效性。结果:通过欧洲某风电场的实际运行数据验证了组合模型的有效性。结果表明,与其他4种单一和组合预测模型相比,本文所建立的组合预测模型具有更高的预测精度。结论:通过某欧洲风电场的实际运行数据验证了组合模型的有效性,结果表明,与其他四种单一和组合预测模型相比,本文组合模型具有更高的预测精度。因此,本文提出的模型可用于预测波动较大的风电功率,有助于为风电场优化调度和储能配置提供支持,从而为电网和风电场降低成本,增加收益。结论:ICEEMDAN分解算法可以有效降低白噪声对模态分解的影响,将不同频率的功率分量放入不同的预测模型中,可以更好地提高风电的预测精度
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ICEEMDAN-based Combined Wind Power Forecasting
Background:: With the depletion of fossil energy and the increasingly serious environmental pollution, the task of developing renewable energy is imminent. As a green and pollutionfree renewable energy, the penetration of wind energy in the power grid continues to rise. Objective:: In order to reduce the volatility and randomness of wind power series and increase the accuracy of wind power prediction, a wind power combination model based on the ICEEMDAN (improved adaptive noise full set empirical mode decomposition) method is proposed. objective: In order to reduce the volatility and randomness of wind power series and increase the accuracy of wind power prediction, a wind power combination model based on ICEEMDAN (improved adaptive noise full set empirical mode decomposition) method is proposed Methodology:: First, the complex original wind power data have been decomposed into several relatively simple subsequences using the ICEEMDAN method. Aiming at the different lengths of coarse grain time series and data loss in traditional multi-scale entropy, a fine composite multiscale dispersion entropy is proposed to calculate the entropy value of each decomposition component, and divide the high- and low-frequency modal components to predict the modal components of different frequencies; secondly, differential moving autoregressive model (ARIMA) and shortterm memory neural network (LSTM) are used to establish the prediction models of high- and low-frequency components, respectively. Results:: Finally, the prediction results of each component have been superimposed and reconstructed to obtain the final prediction results. The effectiveness of the combined model is verified by the actual operation data of a European wind farm. result: The effectiveness of the combined model is verified by the actual operation data of a European wind farm. The results show that compared with the other four single and combined forecasting models, the combined model in this paper has higher forecasting accuracy. Conclusion:: As the effectiveness of the combined model is verified by the actual operation data of a European wind farm, the results have shown that compared to the other four single and combined forecasting models, the combined model in this paper has higher forecasting accuracy. Therefore, the model proposed in this article can be used for predicting wind power with significant fluctuations, which will help to provide support for optimized scheduling and energy storage configuration of wind farms, thereby reducing costs and increasing income for the power grid and wind farms. conclusion: ICEEMDAN decomposition algorithm can effectively reduce the influence of white noise on mode decomposition, and the prediction accuracy of wind power can be better improved by putting different frequency power components into different prediction models
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来源期刊
Recent Patents on Engineering
Recent Patents on Engineering Engineering-Engineering (all)
CiteScore
1.40
自引率
0.00%
发文量
100
期刊介绍: Recent Patents on Engineering publishes review articles by experts on recent patents in the major fields of engineering. A selection of important and recent patents on engineering is also included in the journal. The journal is essential reading for all researchers involved in engineering sciences.
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