SCIENTIA SINICA Informationis, Volume 48 , Issue 10 : 1300-1315(2018) https://doi.org/10.1360/N112018-00075

Decomposition-optimization-ensemble learning approach for electricity price forecasting

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  • ReceivedMar 30, 2018
  • AcceptedMay 25, 2018
  • PublishedOct 9, 2018


Funded by

国家自然科学基金(批准号: 61773401,61304067,11601524,61761130081,11571368)


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  • Figure 1

    (网络版彩图) FEEMD-IWOA-RBF混合学习框架

  • Figure 2

    (网络版彩图) PJM电力市场原始电价数据

  • Figure 3

    (网络版彩图) FEEMD算法分解后的电价序列

  • Figure 4

    (网络版彩图) 预测模型中长期预测相对误差

  • Figure 7

    (网络版彩图) 混合模型短期预测相对误差

  • Figure 8

    (网络版彩图) 模型的RMSE结果对比图

  • Figure 13

    (网络版彩图) 模型的$D_s$结果对比


    Algorithm 1 IWOA算法

    Initialize the whales' population $X_i~(i=1,2,\ldots,n)$ and calculate the fitness of each whale;

    $X^*_{\rm~prey}=$ the fittest whale (a similar position of the prey);

    while $l<$ maximum number of iterations

    for each whale

    Update $a,~A,~C,~t$ and $p$;

    if1 $p<0.5$

    if2 $|A|<1$

    update the position of the current whaleby 13;

    else if2 $|A|>1$

    Select a random whale $X_{\rm~rand}$;

    Update the position of the current whale by 5;

    end if2

    else if1 $p>0.5$

    Update the position of the current whale by 7;

    end if1

    Using quantum method to update the positions of all the whales by 10;

    Choose the better updating method by comparing the fitness of 5, 7, 13 and 10;

    end for

    Check if any whale goes beyond the search space and amend it;

    Calculate the fitness of each whale;

    Update $X^*_{\rm~prey}$ if there is a better solution;


    end while

    return $X^*_{\rm~prey}.$

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