基于EMD-ARMA的组合风光出力预测方法利用emd经验模态分解将原始发电数据分解为多个本征模态函数，采用arma自回归移动平

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基于EMD-ARMA的组合风光出力预测方法 利用emd经验模态分解将原始发电数据分解为多个本征模态函数，采用arma自回归移动平均算法对分量进行分析，通过训练数据建立自回归移动平均模型，将预测分量叠加重构后得到最终风光功率预测结果。 附参考文献
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89867171/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89867171/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于<span class="_ _0"> </span><span class="ff2">EMD-ARMA<span class="_ _1"> </span></span>的组合风光出力预测方法</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">摘要<span class="ff3">：</span>随着可再生能源的快速发展<span class="ff3">，</span>风光发电技术逐渐成为可持续发展的重要组成部分<span class="ff4">。</span>准确预测风</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">光出力对于电力系统的稳定运行和经济调度至关重要<span class="ff4">。</span>本文提出了一种基于经验模态分解<span class="ff3">（</span></div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">Empirical Mode Decomposition<span class="ff3">，</span>EMD<span class="ff3">）<span class="ff1">和自回归移动平均</span>（</span>ARMA<span class="ff3">）<span class="ff1">的组合方法</span>，<span class="ff1">用于风光出</span></span></div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">力的预测<span class="ff4">。</span></div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">引言</span></div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">在现代电力系统中<span class="ff3">，</span>风光发电技术因其清洁<span class="ff4">、</span>可再生的特点受到了广泛关注<span class="ff4">。</span>然而<span class="ff3">，</span>可再生能源的特</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">点导致其出力波动性较大<span class="ff3">，</span>给电力系统的运行和经济调度带来一定的挑战<span class="ff4">。</span>因此<span class="ff3">，</span>准确预测风光出力</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">的技术研究成为了一个热点问题<span class="ff4">。</span></div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">经验模态分解<span class="ff3">（</span></span>EMD<span class="ff3">）<span class="ff1">原理</span></span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">EMD<span class="_ _1"> </span><span class="ff1">是一种信号处理方法<span class="ff3">，</span>可将一个信号分解为多个本征模态函数<span class="ff3">（</span></span>Intrinsic Mode Functions</div><div class="t m0 x1 h2 yc ff3 fs0 fc0 sc0 ls0 ws0">，<span class="ff2">IMFs</span>）<span class="ff4">。<span class="ff2">EMD<span class="_ _1"> </span><span class="ff1">的基本原理是将信号中的高频</span></span>、<span class="ff1">低频成分提取出来</span></span>，<span class="ff1">从而实现信号的分解<span class="ff4">。</span></span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff1">自回归移动平均<span class="ff3">（</span></span>ARMA<span class="ff3">）<span class="ff1">模型</span></span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">ARMA<span class="_ _1"> </span><span class="ff1">模型是一种经典的时间序列模型<span class="ff3">，</span>常用于分析和预测信号或数据序列的特征<span class="ff4">。</span>它的基本思想是</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">将当前时刻的信号值与过去若干时刻的信号值进行加权线性组合来预测未来时刻的信号值<span class="ff4">。</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">4.<span class="_ _2"> </span><span class="ff1">组合风光出力预测方法</span></div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">本文采用<span class="_ _0"> </span><span class="ff2">EMD<span class="_ _1"> </span></span>将原始发电数据分解为多个本征模态函数<span class="ff3">，</span>并借助<span class="_ _0"> </span><span class="ff2">ARMA<span class="_ _1"> </span></span>模型对分量进行分析<span class="ff4">。</span>具体实</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">现过程如下<span class="ff3">：</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">Step 1<span class="ff3">：<span class="ff1">将原始发电数据用<span class="_ _0"> </span></span></span>EMD<span class="_ _1"> </span><span class="ff1">分解成若干个<span class="_ _0"> </span></span>IMFs<span class="ff4">。</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">Step 2<span class="ff3">：<span class="ff1">对每个<span class="_ _0"> </span></span></span>IMF<span class="_ _1"> </span><span class="ff1">应用<span class="_ _0"> </span></span>ARMA<span class="_ _1"> </span><span class="ff1">模型<span class="ff3">，</span>建立自回归移动平均模型<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">Step 3<span class="ff3">：<span class="ff1">根据训练数据训练<span class="_ _0"> </span></span></span>ARMA<span class="_ _1"> </span><span class="ff1">模型<span class="ff3">，</span>得到各个<span class="_ _0"> </span></span>IMF<span class="_ _1"> </span><span class="ff1">的预测模型<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">Step 4<span class="ff3">：<span class="ff1">将各个<span class="_ _0"> </span></span></span>IMF<span class="_ _1"> </span><span class="ff1">的预测结果叠加重构<span class="ff3">，</span>得到最终风光功率的预测结果<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">5.<span class="_ _2"> </span><span class="ff1">实验结果与讨论</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">本文使用某地风光发电数据进行了实验<span class="ff3">，</span>并与传统的预测方法进行了比较<span class="ff4">。</span>实验结果表明<span class="ff3">，</span>基于</div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">EMD-ARMA<span class="_ _1"> </span><span class="ff1">的组合风光出力预测方法相比其他方法具有更高的预测精度和准确性<span class="ff4">。</span></span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">6.<span class="_ _2"> </span><span class="ff1">结论</span></div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0">本文基于<span class="_ _0"> </span><span class="ff2">EMD-ARMA<span class="_ _1"> </span></span>的组合风光出力预测方法<span class="ff3">，</span>通过结合<span class="_ _0"> </span><span class="ff2">EMD<span class="_ _1"> </span></span>和<span class="_ _0"> </span><span class="ff2">ARMA<span class="_ _1"> </span></span>模型的优势<span class="ff3">，</span>实现了对风光</div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0">出力的准确预测<span class="ff4">。</span>实验结果表明<span class="ff3">，</span>该方法具有很高的预测精度和准确性<span class="ff3">，</span>对于电力系统的稳定运行和</div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">经济调度具有重要意义<span class="ff4">。</span></div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">参考文献<span class="ff3">：</span></div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>
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