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/400v_杭州.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import xgboost as xgb
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+def normal(x):
+    high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
+    low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
+    return x[(x<=high)&(x>=low)]
+
+def season(x):
+    if str(x)[5:7] in ('04', '05'):
+        return 0
+    elif str(x)[5:7] in ('01', '02', '03', '06', '09', '10', '11', '12'):
+        return 1
+    else:
+        return 2
+
+df = pd.read_excel('./浙江各地市分电压日电量数据/杭州 .xlsx')
+df = df[['stat_date','0.4kv及以下']]
+df['0.4kv及以下'] = df['0.4kv及以下']/10000
+df['stat_date'] = df['stat_date'].map(lambda x:x.strip())
+df['stat_date'] = pd.to_datetime(df['stat_date'])
+
+
+df_qw = pd.read_excel(r'C:\python-project\p1031\入模数据\杭州.xlsx')
+df_qw.columns = df_qw.columns.map(lambda x:x.strip())
+
+df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
+df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
+
+
+df = pd.merge(df,df_qw,left_on='stat_date',right_on='dtdate',how='left')
+df.drop(columns='dtdate',inplace=True)
+df.set_index('stat_date',inplace=True)
+df['season'] = df.index.map(season)
+df = df.loc[normal(df['0.4kv及以下']).index]
+
+print(df.head())
+
+x_train = df.loc['2022-7':'2023-7'].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-7':'2023-7']['0.4kv及以下']
+x_eval = df.loc['2023-8'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-8']['0.4kv及以下']
+
+x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2,random_state=42)
+model = xgb.XGBRegressor(max_depth=6,learning_rate=0.05,n_estimators=150)
+model.fit(x_train,y_train)
+y_pred = model.predict(x_test)
+print(r2_score(y_test,y_pred))
+
+predict = model.predict(x_eval)
+result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
+print(result)
+print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
+
+# 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}'
+#     print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
+#     if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
+#         list2.append(i)
+#     elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
+#         list0.append(i)
+#     else:
+#         list1.append(i)
+# print(list0,list1,list2)
+

浙江电压等级电量/400v数据预处理.py

@@ -0,0 +1,65 @@
+import pandas as pd
+import os
+
+def normal(x):
+    high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
+    low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
+    return x[(x<=high)&(x>=low)]
+
+
+fir_dir = './浙江各地市分电压日电量数据'
+qw_dir = 'C:\python-project\p1031\入模数据'
+result = pd.DataFrame({})
+for excel,qw_excel in zip(os.listdir(fir_dir),os.listdir(qw_dir)):
+
+    df_city = pd.read_excel(os.path.join(fir_dir,excel))
+
+    df_city = df_city[['stat_date','0.4kv及以下']]
+    df_city['0.4kv及以下'] = df_city['0.4kv及以下']/10000
+    df_city['stat_date'] = df_city['stat_date'].map(lambda x:x.strip())
+    df_city['stat_date'] = pd.to_datetime(df_city['stat_date'])
+
+
+    df_qw = pd.read_excel(os.path.join(qw_dir,qw_excel))
+    df_qw.columns = df_qw.columns.map(lambda x:x.strip())
+
+    df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
+    df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
+
+
+    df = pd.merge(df_city,df_qw,left_on='stat_date',right_on='dtdate',how='left')
+    df.drop(columns='dtdate',inplace=True)
+    df.set_index('stat_date',inplace=True)
+
+
+
+    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}'
+        # print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
+        if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
+            list2.append(i)
+        elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
+            list0.append(i)
+        else:
+            list1.append(i)
+    def season(x):
+        if str(x)[5:7] in list0:
+            return 0
+        elif str(x)[5:7] in list1:
+            return 1
+        else:
+            return 2
+
+
+    df['season'] = df.index.map(season)
+
+    dict1 = {'杭州':0,'湖州':1,'嘉兴':2,'金华':3,'丽水':4,'宁波':5,'衢州':6,'绍兴':7,'台州':8,'温州':9,'舟山':10}
+    df['city'] = dict1[excel[:2]]
+    df.reset_index(inplace=True)
+    result = pd.concat(result,df)
+
+print(df)
+