输出预测结果

浙江电压等级电量/400v_衢州.py

@@ -1,75 +0,0 @@
-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', '06', '11'):
-        return 0
-    elif str(x)[5:7] in ('01', '02', '03', '09', '10', '12'):
-        return 1
-    else:
-        return 2
-
-df = pd.read_excel('./浙江各地市分电压日电量数据/衢州 .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\衢州.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)
-
-
-# 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)
-
-
-df['season'] = df.index.map(season)
-df = df.loc[normal(df['0.4kv及以下']).index]
-
-x_train = df.loc['2021-7':'2023-9'][:-3].drop(columns='0.4kv及以下')
-
-y_train = df.loc['2021-7':'2023-9'][:-3]['0.4kv及以下']
-x_eval = df.loc['2023-9'].drop(columns='0.4kv及以下')
-y_eval = df.loc['2023-9']['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())
-
-
-

浙江电压等级电量/400v各地市模型/400v_丽水.py

@@ -0,0 +1,29 @@
+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
+
+
+df = pd.read_excel('../400v入模数据/丽水.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2021-1':'2023-7'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2021-1':'2023-7'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-7']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_台州.py

@@ -0,0 +1,30 @@
+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
+
+
+
+df = pd.read_excel('../400v入模数据/台州.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-7':'2023-7'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-7':'2023-7'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-7']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_嘉兴.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/嘉兴.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-7':'2023-7'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-7':'2023-7'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-7']['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=158)
+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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_宁波.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/宁波.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-7':'2023-10'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-7':'2023-10'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-10'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-10']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_杭州.py

@@ -8,40 +8,17 @@ def normal(x):
     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 = pd.read_excel('../400v入模数据/杭州.xlsx',index_col='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.index = pd.to_datetime(df.index)
 
-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':'2023-8'].drop(columns='0.4kv及以下')
-y_train = df.loc['2022-7':'2023-8']['0.4kv及以下']
-x_eval = df.loc['2023-9'].drop(columns='0.4kv及以下')
-y_eval = df.loc['2023-9']['0.4kv及以下']
+x_train = df.loc['2022-1':'2023-8'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-1':'2023-8'][:-3]['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)

浙江电压等级电量/400v各地市模型/400v_温州.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/温州.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-7':'2023-7'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-7':'2023-7'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-7']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_湖州.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/湖州.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2021-1':'2023-10'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2021-1':'2023-10'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-10'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-10']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_绍兴.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/绍兴.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2021-1':'2023-10'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2021-1':'2023-10'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-10'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-10']['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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_舟山.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/舟山.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-1':'2023-9'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-1':'2023-9'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-9'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-9']['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=158)
+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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_衢州.py

@@ -0,0 +1,40 @@
+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', '06', '11'):
+        return 0
+    elif str(x)[5:7] in ('01', '02', '03', '09', '10', '12'):
+        return 1
+    else:
+        return 2
+
+df = pd.read_excel('../400v入模数据/衢州.xlsx',index_col='stat_date')
+df.index = pd.to_datetime(df.index)
+
+
+x_train = df.loc['2021-7':'2023-7'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2021-7':'2023-7'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-7'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-7']['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=142)
+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())
+
+
+

浙江电压等级电量/400v各地市模型/400v_金华.py

@@ -0,0 +1,28 @@
+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
+
+df = pd.read_excel('../400v入模数据/金华.xlsx',index_col='stat_date')
+
+df.index = pd.to_datetime(df.index)
+
+x_train = df.loc['2022-1':'2023-10'][:-3].drop(columns='0.4kv及以下')
+y_train = df.loc['2022-1':'2023-10'][:-3]['0.4kv及以下']
+x_eval = df.loc['2023-10'].drop(columns='0.4kv及以下')
+y_eval = df.loc['2023-10']['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())
+
+
+

浙江电压等级电量/400v数据预处理.py

@@ -32,8 +32,6 @@ for excel,qw_excel in zip(os.listdir(fir_dir),os.listdir(qw_dir)):
     df.drop(columns='dtdate',inplace=True)
     df.set_index('stat_date',inplace=True)
 
-
-
     list2 = []
     list0 = []
     list1 = []
@@ -56,7 +54,7 @@ for excel,qw_excel in zip(os.listdir(fir_dir),os.listdir(qw_dir)):
 
     print(f'{excel[:2]}',list0)
     df['season'] = df.index.map(season)
-
+    df.dropna(how='any',inplace=True)
     df.to_excel(f'./400v入模数据/{excel[:2]}.xlsx')
     # 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]]