更新入模数据

.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>

各地级市日电量模型/丽水.py

@@ -5,6 +5,7 @@ from sklearn.metrics import r2_score
 from sklearn.model_selection import train_test_split
 import matplotlib as mpl
 import matplotlib.pyplot as plt
+mpl.rcParams['font.sans-serif']=['kaiti']
 
 def season(x):
     if str(x)[5:7] in ('06','07','08','12','01','02'):
@@ -13,23 +14,18 @@ def season(x):
         return 0
 
 
-mpl.rcParams['font.sans-serif']=['kaiti']
-
 pd.set_option('display.width',None)
-
+parent_dir = os.path.abspath(os.path.join(os.getcwd(),os.pardir))
-data = pd.read_excel(r'C:\Users\user\PycharmProjects\pytorch2\入模数据\丽水数据.xlsx',index_col='dtdate')
+data = pd.read_excel(os.path.join(parent_dir,'入模数据/丽水.xlsx'),index_col='dtdate')
 data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
 data['season'] = data.index.map(season)
-# plt.plot(range(len(data)),data['售电量'])
+
-# plt.show()
 print(data.head())
 
 df_eval = data.loc['2023-9']
 df_train = data.loc['2021-1':'2023-8']
 # df_train = df[500:850]
 print(len(df_eval),len(df_train),len(data))
-
-
 df_train = df_train[['tem_max','tem_min','holiday','24ST','rh','prs','售电量','season']]
 
 # IQR = df['售电量'].describe()['75%'] - df['售电量'].describe()['25%']
@@ -38,6 +34,19 @@ df_train = df_train[['tem_max','tem_min','holiday','24ST','rh','prs','售电量'
 # print('异常值数量：',len(df[(df['售电量'] >= high) | (df['售电量'] <= low)]))
 # df_train = df_train[(df['售电量'] <= high) & (df['售电量'] >= low)]
 
+# list2 = []
+# list0 = []
+# list1 = []
+# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
+#     month_index = df.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
+#     if df.loc[month_index]['售电量'].mean() >= df['售电量'].describe()['75%']:
+#         list2.append(i)
+#     elif df.loc[month_index]['售电量'].mean() <= df['售电量'].describe()['25%']:
+#         list0.append(i)
+#     else:
+#         list1.append(i)
+# print(list0,list1,list2)
+
 
 X = df_train[['tem_max','tem_min','holiday','24ST','season']]
 X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
@@ -46,6 +55,7 @@ y = df_train['售电量']
 # best_i = {}
 # for i in range(200):
 
+
 x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=42)
 model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150)
 model.fit(x_train,y_train)

各地级市日电量模型/杭州.py

@@ -121,6 +121,19 @@ df_eval['city_name'] = df_eval['city_name'].map(lambda x:x.strip())
 df_hangzhou = df_eval[df_eval['city_name']=='金华市'].sort_values(by='dtdate')
 
 
+# list2 = []
+# list0 = []
+# list1 = []
+# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
+#     month_index = df.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
+#     if df.loc[month_index]['售电量'].mean() >= df['售电量'].describe()['75%']:
+#         list2.append(i)
+#     elif df.loc[month_index]['售电量'].mean() <= df['售电量'].describe()['25%']:
+#         list0.append(i)
+#     else:
+#         list1.append(i)
+# print(list0,list1,list2)
+
 print(df_hangzhou)
 
 loaded_model = xgb.XGBRegressor()