基于EEMD Adaboost BP的短期负荷预测

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Wenshuai Lin, Bin Zhang, Hongyi Li, Renquan Lu
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引用次数: 1

摘要

摘要为了实现短时负荷预测,基于集成学习理论,提出了一种具有权值更新机制的Adaboost BP方法。首先,利用集成经验模式分解将原始历史负荷功率分解为一组具有不同特征的子序列。然后,将BP神经网络作为弱学习器来预测测试样本的负载功率。同时,预测结果用于更新弱学习者和测试样本的权重,然后构造强学习者以获得最终的预测结果。根据各子系列特征的分析结果,建立了负荷预测模型。计算实例分析结果表明,该预测模型的精度优于其他算法,具有较高的工程应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short-term load forecasting based on EEMD-Adaboost-BP
ABSTRACT In order to realize short-time load forecasting, an Adaboost-BP method with a weight update mechanism is proposed based on ensemble learning theory. Firstly, the original historical load power is decomposed into a set of sub-series with diverse characteristics via using ensemble empirical mode decomposition. Then, BP neural network is performed as a weak learner to predict the load power of test samples. At the same time, the prediction results are used to update the weight of the weak learner and test sample and then construct a strong learner to obtain the final prediction results. According to the analysis results of the characteristics of each sub-series, the load forecasting model is established. The result of analysing the calculation example shows that the proposed prediction model outperforms all other algorithms in accuracy, which has high engineering application value.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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