diff --git a/入模数据/丽水.xlsx b/入模数据/丽水.xlsx
index a12ca80..92a5c39 100644
Binary files a/入模数据/丽水.xlsx and b/入模数据/丽水.xlsx differ
diff --git a/入模数据/台州.xlsx b/入模数据/台州.xlsx
index 2228f0a..916d6c5 100644
Binary files a/入模数据/台州.xlsx and b/入模数据/台州.xlsx differ
diff --git a/入模数据/嘉兴.xlsx b/入模数据/嘉兴.xlsx
index 1871f1a..c9cb3fe 100644
Binary files a/入模数据/嘉兴.xlsx and b/入模数据/嘉兴.xlsx differ
diff --git a/入模数据/宁波.xlsx b/入模数据/宁波.xlsx
index f9d2d74..0e42293 100644
Binary files a/入模数据/宁波.xlsx and b/入模数据/宁波.xlsx differ
diff --git a/入模数据/杭州.xlsx b/入模数据/杭州.xlsx
index 225e8fb..58fc23c 100644
Binary files a/入模数据/杭州.xlsx and b/入模数据/杭州.xlsx differ
diff --git a/入模数据/温州.xlsx b/入模数据/温州.xlsx
index 221fa89..1d8b688 100644
Binary files a/入模数据/温州.xlsx and b/入模数据/温州.xlsx differ
diff --git a/入模数据/湖州.xlsx b/入模数据/湖州.xlsx
index 4c123db..ea14b56 100644
Binary files a/入模数据/湖州.xlsx and b/入模数据/湖州.xlsx differ
diff --git a/入模数据/绍兴.xlsx b/入模数据/绍兴.xlsx
index 389c392..7f8ebbb 100644
Binary files a/入模数据/绍兴.xlsx and b/入模数据/绍兴.xlsx differ
diff --git a/入模数据/舟山.xlsx b/入模数据/舟山.xlsx
index f202b89..9797ee9 100644
Binary files a/入模数据/舟山.xlsx and b/入模数据/舟山.xlsx differ
diff --git a/入模数据/衢州.xlsx b/入模数据/衢州.xlsx
index 2851332..d6366db 100644
Binary files a/入模数据/衢州.xlsx and b/入模数据/衢州.xlsx differ
diff --git a/入模数据/追加10月数据.py b/入模数据/追加10月数据.py
new file mode 100644
index 0000000..9b5ee2a
--- /dev/null
+++ b/入模数据/追加10月数据.py
@@ -0,0 +1,48 @@
+import pandas as pd
+import math
+import datetime
+import chinese_calendar as cc
+
+df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江各地市气象数据 .xlsx')
+df.columns = df.columns.map(lambda x:x.strip())
+df['dtdate'] = pd.to_datetime(df['dtdate'],format='%Y%m%d').astype('str')
+df['city_name'] = df['city_name'].map(lambda x:x.strip())
+df['city_name'] = df['city_name'].str[:-1]
+df['dtdate'] = df['dtdate'].map(lambda x:x.strip())
+def holiday_work(x):
+    if cc.is_workday(x):
+        return 0
+    if cc.is_holiday(x):
+        return 1
+def jq(y,x):
+    a=365.242 * (y - 1900) + 6.2 + 15.22 * x - 1.9 * math.sin(0.262 * x)
+    return datetime.date(1899,12,31)+datetime.timedelta(days=int(a))
+# print(jq(2023,1))
+jq_list=['小寒', '大寒', '立春', '雨水', '惊蛰', '春分', '清明', '谷雨', '立夏', '小满', '芒种', '夏至', '小暑', '大暑', '立秋', '处暑', '白露', '秋分', '寒露', '霜降', '立冬', '小雪', '大雪','冬至']
+jq_dict={}
+for j in range(2023,2024):
+    for i in range(24):
+        jq_dict[jq(j,i).strftime('%Y-%m-%d')]=jq_list[i]
+# print(jq_dict)
+df['24ST'] = df['dtdate'].map(jq_dict)
+df.fillna(method='ffill',inplace=True)
+df['24ST'].fillna('秋分',inplace=True)
+df['holiday'] = pd.to_datetime(df['dtdate']).map(holiday_work)
+print(df)
+jq_dict = {'冬至': 0, '处暑': 1, '夏至': 2, '大寒': 3, '大暑': 4, '大雪': 5, '寒露': 6, '小寒': 7, '小暑': 8, '小满': 9, '小雪': 10, '惊蛰': 11, '春分': 12, '清明': 13, '白露': 14, '秋分': 15, '立冬': 16, '立夏': 17, '立春': 18, '立秋': 19, '芒种': 20, '谷雨': 21, '雨水': 22, '霜降': 23}
+df_elec = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江省各地市日电量及分压数据21-23年.xlsx',sheet_name=0)
+df_elec.columns = df_elec.columns.map(lambda x:x.strip())
+print(df_elec)
+df_elec['stat_date'] = pd.to_datetime(df_elec['stat_date']).astype(str)
+df_elec['地市'] = df_elec['地市'].map(lambda x:x.strip())
+df_elec['stat_date'] = df_elec['stat_date'].map(lambda x:x.strip())
+df_final = pd.merge(df,df_elec,left_on=['city_name','dtdate'],right_on=['地市','stat_date'],how='left')
+df_final.drop(columns=['stat_date','地市'],inplace=True)
+df_final.rename(columns={'power_sal':'售电量'},inplace=True)
+df_final['24ST'] = df_final['24ST'].map(jq_dict)
+df_final['售电量'] /= 10000
+print(df_final)
+df_final = df_final[['dtdate','city_name','tem_max','tem_min','holiday','24ST','售电量']]
+for city in df_final['city_name'].drop_duplicates():
+    df_city = df_final[df_final['city_name']==city]
+    df_city.to_excel(fr'C:\Users\鸽子\Desktop\追加\{city} .xlsx',index=False)
diff --git a/入模数据/金华.xlsx b/入模数据/金华.xlsx
index d5fca37..ac26c14 100644
Binary files a/入模数据/金华.xlsx and b/入模数据/金华.xlsx differ
diff --git a/各地级市日电量模型/hangzhou.bin b/各地级市日电量模型/hangzhou.bin
index 6620ede..b63515a 100644
Binary files a/各地级市日电量模型/hangzhou.bin and b/各地级市日电量模型/hangzhou.bin differ
diff --git a/各地级市日电量模型/huzhou.bin b/各地级市日电量模型/huzhou.bin
index 4808621..c37fa7d 100644
Binary files a/各地级市日电量模型/huzhou.bin and b/各地级市日电量模型/huzhou.bin differ
diff --git a/各地级市日电量模型/jiaxing.bin b/各地级市日电量模型/jiaxing.bin
index 34c2c42..1d2f3b2 100644
Binary files a/各地级市日电量模型/jiaxing.bin and b/各地级市日电量模型/jiaxing.bin differ
diff --git a/各地级市日电量模型/jinhua.bin b/各地级市日电量模型/jinhua.bin
index 48f2992..9ed8192 100644
Binary files a/各地级市日电量模型/jinhua.bin and b/各地级市日电量模型/jinhua.bin differ
diff --git a/各地级市日电量模型/lishui.bin b/各地级市日电量模型/lishui.bin
index ae8861c..ab1054e 100644
Binary files a/各地级市日电量模型/lishui.bin and b/各地级市日电量模型/lishui.bin differ
diff --git a/各地级市日电量模型/ningbo.bin b/各地级市日电量模型/ningbo.bin
index 3f710d4..249ce3f 100644
Binary files a/各地级市日电量模型/ningbo.bin and b/各地级市日电量模型/ningbo.bin differ
diff --git a/各地级市日电量模型/quzhou.bin b/各地级市日电量模型/quzhou.bin
index a98fc77..1a3d1b4 100644
Binary files a/各地级市日电量模型/quzhou.bin and b/各地级市日电量模型/quzhou.bin differ
diff --git a/各地级市日电量模型/shaoxing.bin b/各地级市日电量模型/shaoxing.bin
index 85652b7..9f1d451 100644
Binary files a/各地级市日电量模型/shaoxing.bin and b/各地级市日电量模型/shaoxing.bin differ
diff --git a/各地级市日电量模型/taizhou.bin b/各地级市日电量模型/taizhou.bin
index db74db6..f25a190 100644
Binary files a/各地级市日电量模型/taizhou.bin and b/各地级市日电量模型/taizhou.bin differ
diff --git a/各地级市日电量模型/wenzhou.bin b/各地级市日电量模型/wenzhou.bin
index df323c8..a23febb 100644
Binary files a/各地级市日电量模型/wenzhou.bin and b/各地级市日电量模型/wenzhou.bin differ
diff --git a/各地级市日电量模型/zhoushan.bin b/各地级市日电量模型/zhoushan.bin
index e941565..8d569a6 100644
Binary files a/各地级市日电量模型/zhoushan.bin and b/各地级市日电量模型/zhoushan.bin differ
diff --git a/各地级市日电量模型/丽水.py b/各地级市日电量模型/丽水.py
index d71cee4..33dce7a 100644
--- a/各地级市日电量模型/丽水.py
+++ b/各地级市日电量模型/丽水.py
@@ -46,14 +46,14 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-1':'2023-8']
-df_train = data[450:900]
+df_train = data[450:-1]
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 # best_goal = 1
 # best_i = {}
@@ -85,7 +85,7 @@ print(goal2)
 # with open(r'C:\Users\user\Desktop\9月各地市日电量预测结果\偏差率.txt','a',encoding='utf-8') as f:
 #     f.write(f'丽水月末3天偏差率：{goal},9号-月底偏差率：{goal2}')
 # # 保存模型
-# model.save_model('lishui.bin')
+model.save_model('lishui.bin')
 import numpy as np
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('lishui.bin')
diff --git a/各地级市日电量模型/台州.py b/各地级市日电量模型/台州.py
index 8144b30..75ffc96 100644
--- a/各地级市日电量模型/台州.py
+++ b/各地级市日电量模型/台州.py
@@ -41,14 +41,14 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-8']
+df_eval = data.loc['2023-10']
 
-df_train = data[500:850]
+df_train = data[500:-1]
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 # best_goal = 1
 # best_i = {}
@@ -72,6 +72,7 @@ goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_ev
 print(goal)
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
+print(result_eval)
 #     if abs(goal) < best_goal:
 #         best_goal = abs(goal)
 #         best_i['best_i'] = i
diff --git a/各地级市日电量模型/嘉兴.py b/各地级市日电量模型/嘉兴.py
index 6936d60..5023762 100644
--- a/各地级市日电量模型/嘉兴.py
+++ b/各地级市日电量模型/嘉兴.py
@@ -41,8 +41,8 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-08']
-df_train = data.iloc[450:900]
+df_eval = data.loc['2023-10']
+df_train = data.iloc[450:-1]
 # df_train = data.loc['2021-01':'2023-07']
 
 
@@ -50,8 +50,8 @@ df_train = data.iloc[450:900]
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 # best_goal = 1
diff --git a/各地级市日电量模型/宁波.py b/各地级市日电量模型/宁波.py
index edd5937..dc29ffc 100644
--- a/各地级市日电量模型/宁波.py
+++ b/各地级市日电量模型/宁波.py
@@ -40,15 +40,17 @@ data = data.loc[normal(data['售电量']).index]
 
 
 data['season'] = data.index.map(season)
-df_eval = data.loc['2023-09']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-01':'2023-08']
-df_train = data[450:900]
+# df_train = data[450:900]
+df_train = data.loc['2022-01':'2023-10'][:-1]
+
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 # best_goal = 1
@@ -72,6 +74,7 @@ print(goal)
 
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
+print(result_eval)
 #     if abs(goal) < best_goal :
 #         best_goal = abs(goal)
 #         best_i['best_i'] = i
@@ -91,3 +94,5 @@ X_eval = np.array([
                        [25.8,14.3,23,0,0]])
 print(model.predict(X_eval))
 
+
+
diff --git a/各地级市日电量模型/杭州.py b/各地级市日电量模型/杭州.py
index 11e7371..cb57f7b 100644
--- a/各地级市日电量模型/杭州.py
+++ b/各地级市日电量模型/杭州.py
@@ -50,10 +50,10 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 
-df_train = data[500:850]
+df_train = data[500:-1]
 
 # df_train = data.loc['2021-01':'2023-08']
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 
 X = df_train[['tem_max','tem_min','24ST','holiday','season']]
 X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
diff --git a/各地级市日电量模型/温州.py b/各地级市日电量模型/温州.py
index 36363a3..0ff0457 100644
--- a/各地级市日电量模型/温州.py
+++ b/各地级市日电量模型/温州.py
@@ -43,16 +43,16 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-1':'2023-8']
-df_train = data[450:900]
+df_train = data[450:-1]
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 # best_goal = 1
diff --git a/各地级市日电量模型/湖州.py b/各地级市日电量模型/湖州.py
index afbc87f..c0baf1c 100644
--- a/各地级市日电量模型/湖州.py
+++ b/各地级市日电量模型/湖州.py
@@ -44,14 +44,14 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2022-6':'2023-8']
-df_train = data[450:900]
+df_train = data[450:-1]
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.1,random_state=158)
@@ -72,7 +72,7 @@ goal = (result_eval['eval'][-3:].sum()-result_eval['pred'][-3:].sum())/result_ev
 print(goal)
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
-
+print(result_eval)
 # 保存模型
 # model.save_model('huzhou.bin')
 loaded_model = xgb.XGBRegressor()
@@ -80,6 +80,7 @@ loaded_model.load_model('huzhou.bin')
 import  numpy as np
 X_eval = np.array([[23.89,15.5,23,1,0],
                    [24.5,13.30,23,0,0],
-                       [25.39,13.5,23,0,0]])
+                       [25.39,13.5,23,0,0]
+                   ])
 print(model.predict(X_eval))
 
diff --git a/各地级市日电量模型/绍兴.py b/各地级市日电量模型/绍兴.py
index 717a07b..204b9d5 100644
--- a/各地级市日电量模型/绍兴.py
+++ b/各地级市日电量模型/绍兴.py
@@ -42,9 +42,9 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-1':'2023-8']
-df_train = data[450:900]
+df_train = data[450:-1]
 print(len(df_eval),len(df_train),len(data))
 
 
@@ -52,8 +52,8 @@ df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 # best_goal = 1
@@ -84,7 +84,7 @@ print(goal2)
 #     best_i['best_i'] = i
 #     x = goal2
 # print(best_i,best_goal,x)
-
+print(result_eval)
 # 保存模型
 # model.save_model('shaoxing.bin')
 loaded_model = xgb.XGBRegressor()
diff --git a/各地级市日电量模型/舟山.py b/各地级市日电量模型/舟山.py
index da636ce..8a20d02 100644
--- a/各地级市日电量模型/舟山.py
+++ b/各地级市日电量模型/舟山.py
@@ -41,13 +41,13 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 
 data['season'] = data.index.map(season)
-df_eval = data.loc['2023-9']
+df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-1':'2023-8']
-df_train = data.iloc[450:900]
+df_train = data.iloc[450:-1]
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.1,random_state=42)
@@ -70,9 +70,9 @@ print(goal)
 
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
+print(result_eval)
 
-
-# model.save_model('zhoushan.bin')
+model.save_model('zhoushan.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('zhoushan.bin')
 import numpy as np
diff --git a/各地级市日电量模型/衢州.py b/各地级市日电量模型/衢州.py
index f55fa17..d79eb16 100644
--- a/各地级市日电量模型/衢州.py
+++ b/各地级市日电量模型/衢州.py
@@ -41,14 +41,14 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 
-df_eval = data.loc['2023-08']
-df_train = data.iloc[450:900]
+df_eval = data.loc['2023-10']
+df_train = data.iloc[450:-1]
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
@@ -66,7 +66,7 @@ print(goal)
 
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
-
+print(result_eval)
 # 保存模型
 # model.save_model('quzhou.bin')
 loaded_model = xgb.XGBRegressor()
diff --git a/各地级市日电量模型/金华.py b/各地级市日电量模型/金华.py
index 7815abf..6ebc109 100644
--- a/各地级市日电量模型/金华.py
+++ b/各地级市日电量模型/金华.py
@@ -41,15 +41,14 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
 
-
-df_eval = data.loc['2023-09']
-# df_train = data.loc['2021-01':'2023-08']
-df_train = data.iloc[450:900]
+df_eval = data.loc['2023-10']
+# df_train = data.loc['2021-01':'2023-10'][:-3]
+df_train = data.iloc[450:-1]
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
-X = df_train[['tem_max','tem_min','holiday','24ST','season']]
-X_eval = df_eval[['tem_max','tem_min','holiday','24ST','season']]
+X = df_train[['tem_max','tem_min','24ST','holiday','season']]
+X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 y = df_train['售电量']
 
 # best_goal = 1
@@ -74,6 +73,8 @@ print(goal)
 
 goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result_eval['eval'].sum()
 print(goal2)
+print(result_eval)
+
     # if abs(goal) < best_goal :
     #     best_goal = abs(goal)
     #     best_i['best_i'] = i
diff --git a/浙江行业电量/行业电量_输出为5.py b/浙江行业电量/行业电量_输出为5.py
index fcd2908..cbb48c4 100644
--- a/浙江行业电量/行业电量_输出为5.py
+++ b/浙江行业电量/行业电量_输出为5.py
@@ -2,8 +2,6 @@ import numpy as np
 import pandas as pd
 import torch
 from torch import nn
-from multiprocessing import Pool
-import matplotlib.pyplot as plt
 import os
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 DAYS_FOR_TRAIN = 10
@@ -54,80 +52,79 @@ def data_preprocessing(data):
     return data
 
 # 拼接数据集
-# file_dir = r'C:\Users\user\Desktop\浙江各地市行业电量数据'
-# excel = os.listdir(file_dir)[0]
-# data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
-# data.drop(columns='地市',inplace=True)
-# data = data_preprocessing(data)
-#
-# df = data[data.columns[0]]
-# df.dropna(inplace = True)
-# dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
-#
-# for level in data.columns[1:]:
-#     df = data[level]
-#     df.dropna(inplace=True)
-#     x, y = create_dataset(df, DAYS_FOR_TRAIN)
-#     dataset_x = np.concatenate((dataset_x, x))
-#     dataset_y = np.concatenate((dataset_y, y))
-#
-#
-# for excel in os.listdir(file_dir)[1:]:
-#
-#     data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
-#     data.drop(columns='地市', inplace=True)
-#     data = data_preprocessing(data)
-#
-#     for level in data.columns:
-#         df = data[level]
-#         df.dropna(inplace=True)
-#         x,y = create_dataset(df,DAYS_FOR_TRAIN)
-#         dataset_x = np.concatenate((dataset_x,x))
-#         dataset_y = np.concatenate((dataset_y,y))
-#
-#
-# print(dataset_x.shape,dataset_y.shape)
+file_dir = r'C:\Users\user\Desktop\浙江各地市行业电量数据'
+excel = os.listdir(file_dir)[0]
+data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col='stat_date')
+data.drop(columns='地市',inplace=True)
+data = data_preprocessing(data)
+
+df = data[data.columns[0]]
+df.dropna(inplace = True)
+dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
+
+for level in data.columns[1:]:
+    df = data[level]
+    df.dropna(inplace=True)
+    x, y = create_dataset(df, DAYS_FOR_TRAIN)
+    dataset_x = np.concatenate((dataset_x, x))
+    dataset_y = np.concatenate((dataset_y, y))
+
+
+for excel in os.listdir(file_dir)[1:]:
+
+    data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col='stat_date')
+    data.drop(columns='地市', inplace=True)
+    data = data_preprocessing(data)
+
+    for level in data.columns:
+        df = data[level]
+        df.dropna(inplace=True)
+        x,y = create_dataset(df,DAYS_FOR_TRAIN)
+        dataset_x = np.concatenate((dataset_x,x))
+        dataset_y = np.concatenate((dataset_y,y))
+
+
+print(dataset_x.shape,dataset_y.shape)
 # # 训练
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 #
 # # 标准化到0~1
-# max_value = np.max(dataset_x)
-# min_value = np.min(dataset_x)
-# dataset_x = (dataset_x - min_value) / (max_value - min_value)
-# dataset_y = (dataset_y - min_value) / (max_value - min_value)
-# print('max_value:',max_value,'min_value:',min_value)
-# # 划分训练集和测试集
-# train_size = int(len(dataset_x)*0.7)
-# train_x = dataset_x[:train_size]
-# train_y = dataset_y[:train_size]
-#
+max_value = np.max(dataset_x)
+min_value = np.min(dataset_x)
+dataset_x = (dataset_x - min_value) / (max_value - min_value)
+dataset_y = (dataset_y - min_value) / (max_value - min_value)
+print('max_value:',max_value,'min_value:',min_value)
+# 划分训练集和测试集
+train_size = int(len(dataset_x)*0.7)
+train_x = dataset_x[:train_size]
+train_y = dataset_y[:train_size]
+
 # # 将数据改变形状，RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
-# train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
-# train_y = train_y.reshape(-1, 1, 5)
-#
+train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
+train_y = train_y.reshape(-1, 1, 5)
+
 # # 转为pytorch的tensor对象
-# train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
-# train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
-#
+train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
+train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
+
 model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device)  # 导入模型并设置模型的参数输入输出层、隐藏层等
-#
-#
-# # train_loss = []
-# # loss_function = nn.MSELoss()
-# # optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
-# # for i in range(1500):
-# #     out = model(train_x)
-# #     loss = loss_function(out, train_y)
-# #     loss.backward()
-# #     optimizer.step()
-# #     optimizer.zero_grad()
-# #     train_loss.append(loss.item())
-# #     if i % 100 == 0:
-# #         print(f'epoch {i+1}: loss:{loss}')
-#
-# # 保存/读取模型
-# # torch.save(model.state_dict(),'hy5.pth')
-#
+
+train_loss = []
+loss_function = nn.MSELoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
+for i in range(1500):
+    out = model(train_x)
+    loss = loss_function(out, train_y)
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+    train_loss.append(loss.item())
+    if i % 100 == 0:
+        print(f'epoch {i+1}: loss:{loss}')
+
+# 保存/读取模型
+torch.save(model.state_dict(),'hy5.pth')
+
 # model.load_state_dict(torch.load('hy5.pth'))
 # # for test
 # model = model.eval()  # 转换成测试模式