基于INGO-BIlstm算法的电力功率负荷预测模型：超参数优化与滑动窗口输入结构的研究与应用,INGO-BIlstm基于改进北方苍鹰优化算法INGO-bilstm，优化超参数 滑动窗口输入结构

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ZIP 基于改进北方苍鹰.zip 大约有13个文件
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基于INGO-BIlstm算法的电力功率负荷预测模型：超参数优化与滑动窗口输入结构的研究与应用,INGO-BIlstm 基于改进北方苍鹰优化算法INGO-bilstm，优化超参数。 滑动窗口输入结构，基于matlab。 电力功率负荷预测，不做任何，效果如下。 可自己替数据和优化算法 ,INGO-Bilstm; 优化超参数; 滑动窗口输入结构; Matlab; 电力功率负荷预测; 数据替换; 算法优化,基于改进算法的电力负荷预测模型
<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/90341597/2/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/90341597/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">基于改进北方苍鹰优化算法<span class="_ _0"> </span></span>INGO-BIlstm<span class="_ _1"> </span><span class="ff2">的超参数优化及其在电力功率负荷预测中的应用</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">一<span class="ff3">、</span>引言</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">随着人工智能技术的飞速发展<span class="ff4">，</span>电力功率负荷预测成为了电力行业研究的热点<span class="ff3">。</span>其中<span class="ff4">，</span>循环神经网络</div><div class="t m0 x1 h2 y4 ff4 fs0 fc0 sc0 ls0 ws0">（<span class="ff1">RNN</span>）<span class="ff2">的变种<span class="ff1">——</span>双向长短期记忆网络</span>（<span class="ff1">BiLSTM</span>）<span class="ff2">因其对序列数据的强大处理能力</span>，<span class="ff2">在电力负荷预</span></div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">测中得到了广泛应用<span class="ff3">。</span>然而<span class="ff4">，</span>如何优化<span class="_ _0"> </span><span class="ff1">BiLstm<span class="_ _1"> </span></span>模型的超参数<span class="ff4">，</span>提高其预测精度<span class="ff4">，</span>一直是研究的重点</div><div class="t m0 x1 h2 y6 ff3 fs0 fc0 sc0 ls0 ws0">。<span class="ff2">本文将探讨一种基于改进北方苍鹰优化算法<span class="ff4">（<span class="ff1">INGO</span>）</span>的<span class="_ _0"> </span><span class="ff1">BiLstm<span class="_ _1"> </span></span>模型超参数优化方法<span class="ff4">，</span>并使用滑动</span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">窗口输入结构<span class="ff4">，</span>在<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _1"> </span></span>环境下进行电力功率负荷预测<span class="ff3">。</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">二<span class="ff3">、<span class="ff1">INGO-BIlstm<span class="_ _1"> </span></span></span>模型及超参数优化</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span>INGO<span class="_ _1"> </span><span class="ff2">算法简介</span></div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">INGO<span class="_ _1"> </span><span class="ff2">算法是一种新型的优化算法<span class="ff4">，</span>其灵感来源于北方苍鹰的捕食行为<span class="ff3">。</span>该算法通过模拟自然界的优</span></div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">化过程<span class="ff4">，</span>能够在复杂的搜索空间中寻找最优解<span class="ff3">。</span></div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span>INGO-BIlstm<span class="_ _1"> </span><span class="ff2">模型构建</span></div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">将<span class="_ _0"> </span><span class="ff1">INGO<span class="_ _1"> </span></span>算法与<span class="_ _0"> </span><span class="ff1">BiLstm<span class="_ _1"> </span></span>模型相结合<span class="ff4">，</span>构建<span class="_ _0"> </span><span class="ff1">INGO-BIlstm<span class="_ _1"> </span></span>模型<span class="ff3">。</span>该模型利用<span class="_ _0"> </span><span class="ff1">INGO<span class="_ _1"> </span></span>算法对<span class="_ _0"> </span><span class="ff1">BiLstm</span></div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">模型的超参数进行优化<span class="ff4">，</span>包括隐藏层数<span class="ff3">、</span>学习率<span class="ff3">、</span>批处理大小等<span class="ff3">。</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">3.<span class="_ _2"> </span><span class="ff2">超参数优化</span></div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">通过<span class="_ _0"> </span><span class="ff1">INGO<span class="_ _1"> </span></span>算法的迭代优化过程<span class="ff4">，</span>寻找最佳的超参数组合<span class="ff4">，</span>以提高<span class="_ _0"> </span><span class="ff1">BiLstm<span class="_ _1"> </span></span>模型在电力功率负荷预测</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">中的性能<span class="ff3">。</span></div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">三<span class="ff3">、</span>滑动窗口输入结构</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">为了更好地捕捉电力功率负荷的时序特性<span class="ff4">，</span>采用滑动窗口输入结构<span class="ff3">。</span>该结构能够根据历史数据和当前</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">数据<span class="ff4">，</span>动态地调整输入序列的长度<span class="ff4">，</span>从而更好地反映电力功率负荷的时变特性<span class="ff3">。</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">四<span class="ff3">、<span class="ff1">Matlab<span class="_ _1"> </span></span></span>实现</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff1">Matlab<span class="_ _1"> </span></span>环境下<span class="ff4">，</span>实现<span class="_ _0"> </span><span class="ff1">INGO-BIlstm<span class="_ _1"> </span></span>模型<span class="ff3">。</span>首先<span class="ff4">，</span>加载电力功率负荷数据<span class="ff4">；</span>然后<span class="ff4">，</span>构建滑动窗口</div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">输入结构<span class="ff4">；</span>接着<span class="ff4">，</span>构建<span class="_ _0"> </span><span class="ff1">BiLstm<span class="_ _1"> </span></span>模型并利用<span class="_ _0"> </span><span class="ff1">INGO<span class="_ _1"> </span></span>算法进行超参数优化<span class="ff4">；</span>最后<span class="ff4">，</span>对优化后的模型进行</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">训练和测试<span class="ff3">。</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">五<span class="ff3">、</span>实验结果与分析</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff2">不做任何优化的<span class="_ _0"> </span></span>BiLstm<span class="_ _1"> </span><span class="ff2">模型效果如下<span class="ff4">（</span>此处以具体数据为准<span class="ff4">）<span class="ff3">。</span></span></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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