输出预测结果

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="pytorch_gpu" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="C:\anaconda\envs\pytorch" project-jdk-type="Python SDK" />
 </project>

.idea/pytorch2.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="pytorch_gpu" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="C:\anaconda\envs\pytorch" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

浙江电压等级电量/区县分压/xgb_区县.py

@@ -0,0 +1,43 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import os
+from sklearn.metrics import r2_score
+import xgboost as xgb
+import matplotlib.pyplot as plt
+pd.set_option('display.width',None)
+
+def normal(s1):
+    high = s1.describe()['75%'] + 1.5*(s1.describe()['75%']-s1.describe()['25%'])
+    low = s1.describe()['25%'] - 1.5 * (s1.describe()['75%'] - s1.describe()['25%'])
+    return s1[(s1>=low)&(s1<=high)]
+
+df = pd.read_csv('区县400v入模数据.csv',encoding='gbk',index_col='dtdate')
+df.index = pd.to_datetime(df.index)
+print(df.head())
+
+# org_name = df['org_name'].values[0]
+org_name = ' 国网温岭市供电公司 '
+data = df[df['org_name']==org_name]
+data = data.loc[normal(data['0.4kv及以下']).index]
+print(data)
+X = data.drop(columns=['city_name','org_name','0.4kv及以下'])
+x = X.loc['2022-1':'2023-7']
+x_eval = X.loc['2023-8']
+y = data['0.4kv及以下'].loc['2022-1':'2023-7']
+y_eval = data['0.4kv及以下'].loc['2023-8']
+plt.plot(range(len(y)),y)
+plt.show()
+
+x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=42)
+
+model = xgb.XGBRegressor(max_depth=6,learning_rate=0.05,n_estimators=150)
+model.fit(x_train,y_train)
+
+pred = model.predict(x_test)
+print(r2_score(pred,y_test))
+
+predict = model.predict(x_eval)
+result = pd.DataFrame({'real':y_eval,'pred':predict},index=y_eval.index)
+print(result)
+print((result['real'][-3:]-result['pred'][-3:]).sum()/result['real'].sum())
+

浙江电压等级电量/区县分压/区县400v数据处理.py

@@ -0,0 +1,24 @@
+import os
+
+import pandas as pd
+
+df = pd.read_csv(r'C:\Users\鸽子\Desktop\浙江各区县数据(2).csv')
+df.columns = df.columns.map(lambda x:x.strip())
+df['市'] = df['市'].str[:2]
+df = df[['市','org_name','日期','0.4kv及以下']]
+df['日期'] = pd.to_datetime(df['日期'])
+print(df)
+
+wd_file = 'C:\python-project\p1031\入模数据'
+df_wd = pd.DataFrame({})
+for city in os.listdir(wd_file):
+    data = pd.read_excel(os.path.join(wd_file,city)).drop(columns='售电量')
+    data['city_name'] = data['city_name'].str[:2]
+    df_wd = pd.concat([df_wd,data])
+print(df_wd)
+df_wd['dtdate'] = pd.to_datetime(df_wd['dtdate'])
+df = pd.merge(df,df_wd,left_on=['日期','市'],right_on=['dtdate','city_name'])
+df = df[['city_name','org_name','dtdate','tem_max','tem_min','holiday','24ST','0.4kv及以下']]
+df['0.4kv及以下'] /= 10000
+df.to_csv('区县400v入模数据.csv',index=False,encoding='GBK')
+print(df)