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 9ed8192..61e2fe0 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/丽水.py b/各地级市日电量模型/丽水.py
index 33dce7a..e536fa7 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
@@ -49,7 +49,7 @@ data['season'] = data.index.map(season)
 df_eval = data.loc['2023-10']
 # df_train = data.loc['2021-1':'2023-8']
 df_train = data[450:-1]
-
+# df_train = data.loc['2022-4':'2023-9'][:-3]
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 X = df_train[['tem_max','tem_min','24ST','holiday','season']]
@@ -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 75ffc96..3ae6660 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
@@ -40,10 +40,12 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 
 data['season'] = data.index.map(season)
-
+# data = data.loc[:'2023-9']
 df_eval = data.loc['2023-10']
 
 df_train = data[500:-1]
+# df_train = data[500:][:-3]
+print(df_train)
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
@@ -79,7 +81,7 @@ print(result_eval)
 # print(best_i,best_goal)
 
 # 保存模型
-# model.save_model('taizhou.bin')
+model.save_model('taizhou.bin')
 import numpy as np
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('taizhou.bin')
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 5023762..78e5f86 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
@@ -40,11 +40,11 @@ data = data.loc[normal(data['售电量']).index]
 #         list1.append(i)
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
-
+# data = data.loc[:'2023-9']
 df_eval = data.loc['2023-10']
 df_train = data.iloc[450:-1]
-# df_train = data.loc['2021-01':'2023-07']
-
+# df_train = data[450:][:-3]
+print(df_train)
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
@@ -83,7 +83,7 @@ print(result_eval)
 # print(best_i,best_goal,x)
 
 # 保存模型
-# model.save_model('jiaxing.bin')
+model.save_model('jiaxing.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('jiaxing.bin')
 import numpy as np
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 dc29ffc..1be7cd3 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
@@ -41,8 +41,8 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 df_eval = data.loc['2023-10']
-# df_train = data.loc['2021-01':'2023-08']
-# df_train = data[450:900]
+# df_train = data.loc['2022-01':'2023-09'][:-3]
+
 df_train = data.loc['2022-01':'2023-10'][:-1]
 
 
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 cb57f7b..95adec6 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
@@ -48,13 +48,12 @@ data = data.loc[normal(data['售电量']).index]
 # data['month'] = data.index.strftime('%Y-%m-%d').str[6]
 # data['month'] = data['month'].astype('int')
 data['season'] = data.index.map(season)
-
-
+# data = data.loc[:'2023-9']
 df_train = data[500:-1]
-
-# df_train = data.loc['2021-01':'2023-08']
+# df_train = data[500:][:-3]
 df_eval = data.loc['2023-10']
 
+print(df_train)
 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']
@@ -96,7 +95,7 @@ print('r2:',r2_score(y_test,y_pred))
 # # 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'杭州月末3天偏差率：{round(goal,5)},9号-月底偏差率：{round(goal2,5)}\n')
 # 保存模型
-# model.save_model('hangzhou.bin')
+model.save_model('hangzhou.bin')
 
 # X_eval = df_eval[['tem_max','tem_min','24ST','holiday','season']]
 # df_eval = pd.read_excel(r'C:\Users\user\Desktop\浙江气象1027.xlsx')
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 0ff0457..ad2a1f5 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
@@ -42,15 +42,15 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
 
-
+# data = data.loc[:'2023-8']
 df_eval = data.loc['2023-10']
-# df_train = data.loc['2021-1':'2023-8']
-df_train = data[450:-1]
 
+df_train = data[450:-1]
+# df_train = data[450:][:-3]
+print(df_train)
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','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']
@@ -77,14 +77,14 @@ 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
 #     x = goal2
 # print(best_i,best_goal,x)
 # 保存模型
-# model.save_model('wenzhou.bin')
+model.save_model('wenzhou.bin')
 
 loaded_model = xgb.XGBRegressor()
 
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 c0baf1c..7edc090 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
@@ -43,10 +43,12 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 
 data['season'] = data.index.map(season)
-
+# data = data.loc[:'2023-9']
 df_eval = data.loc['2023-10']
-# df_train = data.loc['2022-6':'2023-8']
+
 df_train = data[450:-1]
+# df_train = data[450:][:-3]
+print(df_train)
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
 
@@ -74,7 +76,7 @@ goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result
 print(goal2)
 print(result_eval)
 # 保存模型
-# model.save_model('huzhou.bin')
+model.save_model('huzhou.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('huzhou.bin')
 import  numpy as np
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 204b9d5..a192be4 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
@@ -43,15 +43,14 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 df_eval = data.loc['2023-10']
-# df_train = data.loc['2021-1':'2023-8']
+# data = data.loc[:'2023-8']
 df_train = data[450:-1]
-print(len(df_eval),len(df_train),len(data))
+# df_train = data[450:][:-3]
+print(df_train)
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','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']
@@ -86,7 +85,7 @@ print(goal2)
 # print(best_i,best_goal,x)
 print(result_eval)
 # 保存模型
-# model.save_model('shaoxing.bin')
+model.save_model('shaoxing.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('shaoxing.bin')
 import numpy as np
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 8a20d02..c94bbe9 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
@@ -42,8 +42,9 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 df_eval = data.loc['2023-10']
-# df_train = data.loc['2021-1':'2023-8']
+# data = data.loc[:'2023-8']
 df_train = data.iloc[450:-1]
+# df_train = data.iloc[450:][:-3]
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 X = df_train[['tem_max','tem_min','24ST','holiday','season']]
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 d79eb16..dc5314c 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
@@ -39,10 +39,11 @@ data = data.loc[normal(data['售电量']).index]
 #
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
-
+# data = data.loc[:'2023-8']
 
 df_eval = data.loc['2023-10']
 df_train = data.iloc[450:-1]
+# df_train = data.iloc[450:-3]
 
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
@@ -68,7 +69,7 @@ goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result
 print(goal2)
 print(result_eval)
 # 保存模型
-# model.save_model('quzhou.bin')
+model.save_model('quzhou.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('quzhou.bin')
 import numpy as np
@@ -79,7 +80,6 @@ X_eval = np.array([[23.69,16.5,23,1,0],
 print(model.predict(X_eval))
 
 
-
 # import torch
 # from torch import nn
 # from torch.utils.data import TensorDataset,DataLoader
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 6ebc109..1cb6eb8 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
@@ -41,9 +41,12 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-10']
-# df_train = data.loc['2021-01':'2023-10'][:-3]
-df_train = data.iloc[450:-1]
+data = data.loc[:'2023-9']
+df_eval = data.loc['2023-9']
+
+# df_train = data.iloc[450:-1]
+df_train = data.iloc[450:-3]
+print(df_train)
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
 
@@ -81,7 +84,7 @@ print(result_eval)
     #     x = goal2
 
 # # 保存模型
-# model.save_model('jinhua.bin')
+model.save_model('jinhua.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('jinhua.bin')
 import numpy as np
diff --git a/江苏年度电量/X12-ARIMA.py b/江苏年度电量/X12-ARIMA.py
new file mode 100644
index 0000000..66c005e
--- /dev/null
+++ b/江苏年度电量/X12-ARIMA.py
@@ -0,0 +1,19 @@
+from statsmodels.tsa.x13 import x13_arima_analysis
+import pandas as pd
+import os
+# 读取时间序列数据
+data = pd.read_excel('./江苏省日电量数据.xlsx', index_col='pt_date', parse_dates=True)
+data = data['power_sal']
+
+# 调用X12-ARIMA模型进行季节性调整和预测
+result = x13_arima_analysis(data)
+
+# 输出调整后的时间序列数据
+adjusted_data = result.seasadj
+
+# 输出预测结果
+forecasts = result.forecasts
+
+# 打印结果
+print(adjusted_data)
+print(forecasts)
\ No newline at end of file
diff --git a/江苏年度电量/江苏省日电量数据.xlsx b/江苏年度电量/江苏省日电量数据.xlsx
new file mode 100644
index 0000000..d672fdf
Binary files /dev/null and b/江苏年度电量/江苏省日电量数据.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/丽水.xlsx b/浙江行业电量/浙江各地市行业电量数据/丽水.xlsx
new file mode 100644
index 0000000..1834166
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diff --git a/浙江行业电量/浙江各地市行业电量数据/台州.xlsx b/浙江行业电量/浙江各地市行业电量数据/台州.xlsx
new file mode 100644
index 0000000..71f05d8
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diff --git a/浙江行业电量/浙江各地市行业电量数据/嘉兴.xlsx b/浙江行业电量/浙江各地市行业电量数据/嘉兴.xlsx
new file mode 100644
index 0000000..50d9ea1
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/嘉兴.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/宁波.xlsx b/浙江行业电量/浙江各地市行业电量数据/宁波.xlsx
new file mode 100644
index 0000000..9a38dd2
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/宁波.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/杭州.xlsx b/浙江行业电量/浙江各地市行业电量数据/杭州.xlsx
new file mode 100644
index 0000000..c9e6792
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diff --git a/浙江行业电量/浙江各地市行业电量数据/温州.xlsx b/浙江行业电量/浙江各地市行业电量数据/温州.xlsx
new file mode 100644
index 0000000..ed1ac87
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/温州.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/湖州.xlsx b/浙江行业电量/浙江各地市行业电量数据/湖州.xlsx
new file mode 100644
index 0000000..2d26a62
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/湖州.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/绍兴.xlsx b/浙江行业电量/浙江各地市行业电量数据/绍兴.xlsx
new file mode 100644
index 0000000..9593a4d
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diff --git a/浙江行业电量/浙江各地市行业电量数据/舟山.xlsx b/浙江行业电量/浙江各地市行业电量数据/舟山.xlsx
new file mode 100644
index 0000000..e28876c
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/舟山.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/衢州.xlsx b/浙江行业电量/浙江各地市行业电量数据/衢州.xlsx
new file mode 100644
index 0000000..863b296
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/衢州.xlsx differ
diff --git a/浙江行业电量/浙江各地市行业电量数据/金华.xlsx b/浙江行业电量/浙江各地市行业电量数据/金华.xlsx
new file mode 100644
index 0000000..0c8f2ef
Binary files /dev/null and b/浙江行业电量/浙江各地市行业电量数据/金华.xlsx differ
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 cbb48c4..9ec210e 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
@@ -3,6 +3,9 @@ import pandas as pd
 import torch
 from torch import nn
 import os
+import time
+t1 = time.time()
+
 os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 DAYS_FOR_TRAIN = 10
 torch.manual_seed(42)
@@ -38,8 +41,7 @@ def normal(nd):
 
 def data_preprocessing(data):
     data.columns = data.columns.map(lambda x: x.strip())
-    data.index = data.index.map(lambda x:x.strip())
-
+    data.index = data.index.map(lambda x:str(x).strip()[:10])
     data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
     data.sort_index(inplace=True)
     data = data.loc['2021-01':'2023-08']
@@ -52,78 +54,78 @@ 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 = './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]
-
-# # 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)
-
-# # 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)
-
+# 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)
+#
+# # # 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)
+#
 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
@@ -148,7 +150,7 @@ max_value = 354024930.8
 min_value = 0.0
 
 # 创建测试集
-file_dir = r'C:\Users\鸽子\Desktop\浙江各地市行业电量数据'
+file_dir = './浙江各地市行业电量数据'
 for excel in os.listdir(file_dir):
     df_city = pd.read_excel(os.path.join(file_dir,excel))
     city = df_city['地市'].iloc[0]
@@ -164,7 +166,8 @@ for excel in os.listdir(file_dir):
         result = pred.detach().numpy()[-5:-2]
         result_dict[industry] = list(result)
     df = pd.DataFrame(result_dict,index=['2023-10-29','2023-10-30','2023-10-31'])
-    df.to_excel(fr'C:\Users\鸽子\Desktop\行业电量预测29-31\{city}.xlsx')
+    # df.to_excel(fr'./行业电量预测29-31/{city}.xlsx')
+print(time.time()-t1)
 print(result_dict)
 
 # 反归一化