基于CNN-RNN架构的高精度时间序列预测程序：风电功率与电力负荷预测利器，清晰注释，轻松换数据训练分析,基于CNN-RNN架构的高精度时间序列预测程序：风电功率与电力负荷预测利器，注释清晰可快速上手
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基于CNN-RNN架构的高精度时间序列预测程序：风电功率与电力负荷预测利器，清晰注释，轻松换数据训练分析,基于CNN-RNN架构的高精度时间序列预测程序：风电功率与电力负荷预测利器，注释清晰可快速上手，精确实现训练与测试精度分析。,基于（CNN-RNN）的时间序列预测程序，预测精度很高。可用于做风电功率预测，电力负荷预测等等标记注释清楚，可直接数据运行。代码实现训练与测试精度分析。,核心关键词：CNN-RNN; 时间序列预测程序; 预测精度高; 风电功率预测; 电力负荷预测; 标记注释清楚; 代码实现; 训练与测试精度分析。,基于CNN-RNN的精准时间序列预测程序：风电电力负荷预测分析系统 <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/90432020/2/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/90432020/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">基于<span class="_ _0"> </span></span>CNN-RNN<span class="_ _0"> </span><span class="ff2">的时间序列预测模型</span>——<span class="ff2">迈向电力行业新篇章</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">大家好，<span class="_ _1"></span>我是一名在技术海洋中乘风破浪的探索者。<span class="_ _1"></span>今天，<span class="_ _1"></span>我要与大家分享一个我在时间序</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">列预测领域的发现<span class="ff1">——</span>基于<span class="_ _0"> </span><span class="ff1">CNN-RNN</span>（卷积神经网络与循环神经网络）<span class="_ _1"></span>的模型。<span class="_ _1"></span>该模型在</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">风电功率预测和电力负荷预测等场景中，展现了惊人的预测精度。</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">一、背景介绍</div><div class="t m0 x1 h2 y6 ff2 fs0 fc0 sc0 ls0 ws0">随着智能电网的快速发展，<span class="_ _2"></span>电力行业对时间序列预测的需求日益增长。<span class="_ _2"></span>无论是风电功率的预</div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">测还是电力负荷的预测，<span class="_ _1"></span>都需要一个能够准确捕捉时间序列数据特征的模型。<span class="_ _1"></span>而<span class="_ _0"> </span><span class="ff1">CNN-RNN</span></div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">模型，<span class="_ _3"></span>正是这样一种能够融合卷积神经网络的空间特征提取能力和循环神经网络的时序依赖</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">关系捕捉能力的模型。</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">二、<span class="ff1">CNN-RNN<span class="_ _0"> </span></span>模型详解</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">让我<span class="_ _4"></span>们从<span class="_ _4"></span>底层<span class="_ _4"></span>开始<span class="_ _4"></span>理解<span class="_ _4"></span>这个强<span class="_ _4"></span>大的<span class="_ _4"></span>模型<span class="_ _4"></span>。首<span class="_ _4"></span>先，<span class="_ _4"></span>卷积<span class="_ _4"></span>神经<span class="_ _4"></span>网络<span class="_ _4"></span>（<span class="ff1">CNN<span class="_ _4"></span></span>）可<span class="_ _4"></span>以有<span class="_ _4"></span>效地<span class="_ _4"></span>从原<span class="_ _4"></span>始</div><div class="t m0 x1 h2 yc ff2 fs0 fc0 sc0 ls0 ws0">数据中提取空间特征，<span class="_ _1"></span>这对于时间序列数据中的模式识别和特征提取非常有帮助。<span class="_ _1"></span>其次，<span class="_ _1"></span>循</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">环神经网络<span class="_ _1"></span>（<span class="ff1">RNN</span>）<span class="_ _5"></span>可以很好地处理时间序列数据的时序依赖关系，<span class="_ _5"></span>能够预测下一个时刻的</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">输出。通过结合这两者的优点，<span class="ff1">CNN-RNN<span class="_ _0"> </span></span>模型在时间序列预测上取得了突破性的进展。</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">三、代码实现与注释</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">下面<span class="_ _4"></span>是一<span class="_ _4"></span>个简<span class="_ _4"></span>单的<span class="_ _6"> </span><span class="ff1">CNN-RNN<span class="_"> </span></span>模型实<span class="_ _4"></span>现示<span class="_ _4"></span>例，<span class="_ _4"></span>我会<span class="_ _4"></span>尽量<span class="_ _4"></span>标记<span class="_ _4"></span>清楚<span class="_ _4"></span>每一<span class="_ _4"></span>行代<span class="_ _4"></span>码的<span class="_ _4"></span>注释<span class="_ _4"></span>，以<span class="_ _4"></span>便</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">大家能够直接换上自己的数据进行运行。</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">```python</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0"># <span class="_ _7"> </span><span class="ff2">导入必要的库</span></div><div class="t m0 x1 h2 y14 ff1 fs0 fc0 sc0 ls0 ws0">import numpy as np</div><div class="t m0 x1 h2 y15 ff1 fs0 fc0 sc0 ls0 ws0">import tensorflow as tf</div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">from tensorflow.keras.models import Model</div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">from tensorflow.keras.layers import Dense, Conv1D, LSTM, Dropout</div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0"># <span class="_ _7"> </span><span class="ff2">假设我们有一组时间序列数据</span> <span class="_ _7"> </span>X <span class="_ _7"> </span><span class="ff2">和其对应的标签</span> <span class="_ _7"> </span>Y</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0"># X.shape = (num_samples, num_timesteps, num_features)</div><div class="t m0 x1 h2 y1a ff1 fs0 fc0 sc0 ls0 ws0"># Y.shape = (num_samples, num_timesteps)</div><div class="t m0 x1 h2 y1b ff1 fs0 fc0 sc0 ls0 ws0"># <span class="_ _7"> </span><span class="ff2">以下为模型构建代码</span></div><div class="t m0 x1 h2 y1c ff1 fs0 fc0 sc0 ls0 ws0"># <span class="_ _7"> </span><span class="ff2">构建卷积层，用于提取空间特征</span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">conv_layer <span class="_ _8"> </span>= <span class="_ _8"> </span>Conv1D(filters=64, <span class="_ _8"> </span>kernel_size=3, <span class="_ _8"> </span>activation='relu', <span class="_ _8"> </span>input_shape=(None, <span class="_ _8"></span>1)) <span class="_ _8"></span># </div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">None <span class="_ _7"> </span><span class="ff2">为任意的时间步长，输入维度为时间步数</span> <span class="_ _7"> </span>x <span class="_ _7"> </span><span class="ff2">特征数</span></div><div class="t m0 x1 h2 y1f ff1 fs0 fc0 sc0 ls0 ws0"># <span class="_ _7"> </span><span class="ff2">构建循环层，用于捕捉时序依赖关系</span></div><div class="t m0 x1 h2 y20 ff1 fs0 fc0 sc0 ls0 ws0">rnn_layer = LSTM(units=32, return_sequences=True) # <span class="_ _7"> </span><span class="ff2">构建<span class="_ _7"> </span></span>LSTM<span class="_"> </span><span class="ff2">层，<span class="_ _9"></span>并选择是否返回完整</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">的序列（默认为<span class="_ _0"> </span><span class="ff1">True</span>）</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>