diff --git a/iceemdan信号重构.ipynb b/iceemdan信号重构.ipynb
deleted file mode 100644
index e1743f9..0000000
--- a/iceemdan信号重构.ipynb
+++ /dev/null
@@ -1,429 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "from math import sqrt\n",
- "from numpy import concatenate\n",
- "from matplotlib import pyplot\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from sklearn.preprocessing import MinMaxScaler\n",
- "from sklearn.preprocessing import LabelEncoder\n",
- "from sklearn.metrics import mean_squared_error\n",
- "from tensorflow.keras import Sequential\n",
- "\n",
- "from tensorflow.keras.layers import Dense\n",
- "from tensorflow.keras.layers import LSTM\n",
- "from tensorflow.keras.layers import Dropout\n",
- "from sklearn.model_selection import train_test_split\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 加载数据\n",
- "path1 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\低频_forecast.csv\"#数据所在路径\n",
- "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
- "f_low= pd.DataFrame(pd.read_csv(path1))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 加载数据\n",
- "path2 = r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\高频re_forecast.csv\"#数据所在路径\n",
- "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
- "f_high= pd.DataFrame(pd.read_csv(path2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "path3= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\低频_test.csv\"#数据所在路径\n",
- "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
- "true_low= pd.DataFrame(pd.read_csv(path3))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [],
- "source": [
- "path4= r\"D:\\project\\小论文1-基于ICEEMDAN分解的时序高维变化的短期光伏功率预测模型\\CEEMAN-PosConv1dbiLSTM-LSTM\\模型代码流程\\完整的模型代码流程\\高频re_test.csv\"#数据所在路径\n",
- "#我的数据是excel表，若是csv文件用pandas的read_csv()函数替换即可。\n",
- "true_high= pd.DataFrame(pd.read_csv(path4))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
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