输出预测结果

各地级市日电量模型/丽水.py

@@ -47,7 +47,7 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 # df_train = data.loc['2021-1':'2023-8']
 df_train = data[450:-1]
 # df_train = data.loc['2022-4':'2023-9'][:-3]
@@ -71,7 +71,7 @@ print(abs(y_test - y_pred).mean() / y_test.mean())
 eval_pred = model.predict(X_eval)
 
 result_eval = pd.DataFrame({'eval': df_eval['售电量'], 'pred': eval_pred}, index=df_eval['售电量'].index)
-print(result_eval)
+print(result_eval['eval'])
 
 goal = (result_eval['eval'][-3:].sum() - result_eval['pred'][-3:].sum()) / result_eval['eval'].sum()
 print(goal)
@@ -85,10 +85,14 @@ print(goal2)
 import numpy as np
 
 X_eval = np.array([
-    [21,10,10,0,0],
-    [21, 11, 10, 0, 0],
-    [20, 8, 10, 0, 0],
-    [20, 8, 10, 0, 0],
-    [17, 10, 10, 0, 0]
+    [17.2, 5.7, 10, 0, 0],
+    [21.2, 4.3, 10, 0, 0],
+    [11.5, 6.6, 10, 0, 0]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)
+

各地级市日电量模型/台州.py

@@ -41,7 +41,7 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 # data = data.loc[:'2023-9']
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 
 df_train = data[500:-1]
 # df_train = data[500:][:-3]
@@ -85,10 +85,13 @@ model.save_model('taizhou.bin')
 import numpy as np
 
 X_eval = np.array([
-    [19,11,10,1,0],
-    [21, 7, 10, 0, 0],
-    [19, 5, 10, 0, 0],
-    [17, 8, 10, 0, 0],
-    [16, 7, 10, 0, 0]
+    [18.8, 6.2, 10, 0, 0],
+    [21.7, 6.5, 10, 0, 0],
+    [14.3, 8.4, 10, 0, 0]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/嘉兴.py

@@ -44,7 +44,7 @@ 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_eval = data.loc['2023-11']
 df_train = data.iloc[450:-1]
 # df_train = data[450:][:-3]
 print(df_train)
@@ -89,10 +89,13 @@ loaded_model.load_model('jiaxing.bin')
 import numpy as np
 
 X_eval = np.array([
-    [17, 9, 10, 0, 1],
-    [16, 3, 10, 0, 1],
-    [15, 3, 10, 0, 1],
-    [15, 7, 10, 0, 1],
-    [14, 5, 10, 0, 1]
+    [14.5, 7.7, 10, 0, 1],
+    [18.2, 7.8, 10, 0, 1],
+    [11.9, 6.6, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/宁波.py

@@ -45,10 +45,10 @@ data = data.loc[normal(data['售电量']).index]
 
 
 data['season'] = data.index.map(season)
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 # df_train = data.loc['2022-01':'2023-09'][:-3]
 
-df_train = data.loc['2022-01':'2023-10'][:-1]
+df_train = data.loc['2022-01':'2023-11']
 
 df_train = df_train[['tem_max', 'tem_min', 'holiday', '24ST', '售电量', 'season']]
 
@@ -78,22 +78,21 @@ 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('ningbo.bin')
 import numpy as np
 
 X_eval = np.array([
-    [20, 10, 10, 0, 1],
-    [19, 12, 10, 0, 1],
-    [18, 9, 10, 0, 1],
-    [21, 9, 10, 0, 1],
-    [14, 9, 10, 0, 1]
+
+    [16.5, 6.8, 10, 0, 1],
+    [21.7, 6.8, 10, 0, 1],
+    [13, 8.8, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/杭州.py

@@ -58,7 +58,7 @@ data['season'] = data.index.map(season)
 # data = data.loc[:'2023-9']
 df_train = data[500:-1]
 # df_train = data[500:][:-3]
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 
 print(df_train)
 X = df_train[['tem_max', 'tem_min', '24ST', 'holiday', 'season']]
@@ -115,10 +115,13 @@ model.save_model('hangzhou.bin')
 loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('hangzhou.bin')
 X_eval = np.array([
-    [18, 9, 10, 0, 0],
-    [20, 7, 10, 0, 0],
-    [17, 4, 10, 0, 0],
-    [15, 8, 10, 0, 0],
-    [12, 7, 10, 0, 0]
+    [17.2, 5.7, 10, 0, 0],
+    [21.2, 4.3, 10, 0, 0],
+    [11.5, 6.6, 10, 0, 0]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/温州.py

@@ -46,9 +46,9 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 # data = data.loc[:'2023-8']
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 
-df_train = data[450:-1]
+df_train = data[450:]
 # df_train = data[450:][:-3]
 print(df_train)
 
@@ -95,10 +95,13 @@ loaded_model.load_model('wenzhou.bin')
 import numpy as np
 
 X_eval = np.array([
-    [23, 11, 10, 0, 1],
-    [21, 8, 10, 0, 1],
-    [21, 8, 10, 0, 1],
-    [21, 9, 10, 0, 1],
-    [17, 7, 10, 0, 1]
+    [19.8, 6.6, 10, 0, 1],
+    [22, 6.1, 10, 0, 1],
+    [18.5, 10.1, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/湖州.py

@@ -47,7 +47,7 @@ data = data.loc[normal(data['售电量']).index]
 
 data['season'] = data.index.map(season)
 # data = data.loc[:'2023-9']
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 
 df_train = data[450:-1]
 # df_train = data[450:][:-3]
@@ -83,10 +83,14 @@ loaded_model.load_model('huzhou.bin')
 import numpy as np
 
 X_eval = np.array([
-    [15, 8, 10, 0, 1],
-    [18, 9, 10, 0, 1],
-    [15, 8, 10, 0, 1],
-    [16, 8, 10, 0, 1],
-    [12, 7, 10, 0, 1]
+
+    [14.9, 7.1, 10, 0, 1],
+    [17.7, 6.6, 10, 0, 1],
+    [10.3, 5.8, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/绍兴.py

@@ -47,7 +47,7 @@ data['season'] = data.index.map(season)
 
 df_eval = data.loc['2023-11']
 # data = data.loc[:'2023-8']
-df_train = data[450:-1]
+df_train = data[450:]
 # df_train = data[450:][:-3]
 print(df_train)
 
@@ -57,10 +57,6 @@ 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 = {}
-# for i in range(400):
-
 x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
 model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150)
 model.fit(x_train, y_train)
@@ -79,11 +75,7 @@ print(goal)
 
 goal2 = (result_eval['eval'][-23:].sum() - result_eval['pred'][-23:].sum()) / result_eval['eval'].sum()
 print(goal2)
-# if abs(goal) < best_goal :
-#     best_goal = abs(goal)
-#     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')
@@ -92,10 +84,13 @@ print(result_eval)
 import numpy as np
 
 X_eval = np.array([
-    [16.2, 8.2, 10, 1, 0],
-    [20, 6, 10, 0, 0],
-    [19, 7, 10, 0, 0],
-    [16, 8, 10, 0, 0],
-    [12, 7, 10, 0, 0]
+    [17.4, 6.6, 10, 0, 0],
+    [21.2, 7, 10, 0, 0],
+    [12.1, 7.3, 10, 0, 0]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/舟山.py

@@ -44,9 +44,9 @@ data = data.loc[normal(data['售电量']).index]
 # print(list0,list1,list2)
 
 data['season'] = data.index.map(season)
-df_eval = data.loc['2023-10']
+df_eval = data.loc['2023-11']
 # data = data.loc[:'2023-8']
-df_train = data.iloc[450:-1]
+df_train = data.iloc[450:]
 # df_train = data.iloc[450:][:-3]
 
 df_train = df_train[['tem_max', 'tem_min', 'holiday', '24ST', '售电量', 'season']]
@@ -80,10 +80,13 @@ loaded_model.load_model('zhoushan.bin')
 import numpy as np
 
 X_eval = np.array([
-    [18, 11, 10, 0, 1],
-    [17, 9, 10, 0, 1],
-    [17, 8, 10, 0, 1],
-    [18, 10, 10, 0, 1],
-    [14, 7, 10, 0, 1]
+    [14.7, 11.4, 10, 0, 1],
+    [19.4, 11.8, 10, 0, 1],
+    [14.9, 9.4, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

各地级市日电量模型/衢州.py

@@ -41,8 +41,8 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 # data = data.loc[:'2023-8']
 
-df_eval = data.loc['2023-10']
-df_train = data.iloc[450:-1]
+df_eval = data.loc['2023-11']
+df_train = data.iloc[450:]
 # df_train = data.iloc[450:-3]
 
 
@@ -74,14 +74,16 @@ loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('quzhou.bin')
 import numpy as np
 X_eval = np.array([
-    [19,10,10,0,1],
-    [19, 7, 10, 0, 1],
-    [18, 6, 10, 0, 1],
-    [16, 7, 10, 0, 1],
-    [14, 10, 10, 0, 1]
+    [18.7, 7, 10, 0, 1],
+    [20.2, 6.5, 10, 0, 1],
+    [11.2, 8, 10, 0, 1]
 ])
 print(model.predict(X_eval))
-
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)
 
 
 # import torch

各地级市日电量模型/金华.py

@@ -41,11 +41,11 @@ 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['2023-9']
+data = data.loc[:'2023-11']
+df_eval = data.loc['2023-11']
 
 # df_train = data.iloc[450:-1]
-df_train = data.iloc[450:-3]
+df_train = data.iloc[450:]
 print(df_train)
 
 df_train = df_train[['tem_max','tem_min','holiday','24ST','售电量','season']]
@@ -89,10 +89,13 @@ loaded_model = xgb.XGBRegressor()
 loaded_model.load_model('jinhua.bin')
 import numpy as np
 X_eval = np.array([
-    [19,12,10,0,1],
-    [20, 10, 10, 0, 1],
-    [18, 5, 10, 0, 1],
-    [17, 6, 10, 0, 1],
-    [14, 8, 10, 0, 1]
+    [19.5, 7.6, 10, 0, 1],
+    [21.7, 6.8, 10, 0, 1],
+    [11.6, 8.2, 10, 0, 1]
 ])
 print(model.predict(X_eval))
+result = model.predict(X_eval)
+result = pd.DataFrame(result,index=['2023-11-28','2023-11-29','2023-11-30'])
+result = pd.concat((result_eval['eval'],result))
+result.index = result.index.map(lambda x:str(x)[:10])
+print(result)

浙江电压等级电量/电压等级_输入10_输出3.py

@@ -166,7 +166,7 @@ max_value,min_value = 192751288.47,0.0
 model.load_state_dict(torch.load('best_dy3.pth',map_location=torch.device('cpu')))              # cpu跑加上,map_location=torch.device('cpu')
 # file_dir = r'./浙江各地市分电压日电量数据'
 
-df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231127.xlsx',sheet_name=1)
+df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231129.xlsx',sheet_name=1)
 df = df[df['county_name'].isnull()]
 
 for city in df['city_name'].drop_duplicates():
@@ -182,8 +182,14 @@ for city in df['city_name'].drop_duplicates():
         pred = pred * (max_value - min_value) + min_value
         result = pred.cpu().detach().numpy()[-3:]
         result_dict[level] = list(result)
+
     df1 = pd.DataFrame(result_dict,index=['2023-11-28','2023-11-29','2023-11-30'])
-    df1.to_excel(fr'C:\Users\鸽子\Desktop\11月分压电量预测28-30\{city} .xlsx')
+    df1['city_name'] = city
+    df1 = df1[df_city.columns]
+    df1 = pd.concat((df_city.iloc[:27],df1))
+
+    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市分压电量预测v1129.xlsx',mode='a',engine='openpyxl',if_sheet_exists='replace') as writer:
+        df1.to_excel(writer,sheet_name=f'{city[4:6]}')
 print(result_dict)
 
 # 打印指标

浙江行业电量/行业电量_输出为3_步长为10.py

@@ -164,7 +164,7 @@ model = model.eval()
 
 # 测试
 # file_dir = './浙江各地市行业电量数据'
-df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231127.xlsx',sheet_name=2)
+df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231129.xlsx',sheet_name=2)
 for city in df['city_name'].drop_duplicates():
     df_city = df[df['city_name']==city].sort_values(by='stat_date').set_index('stat_date')
 
@@ -183,7 +183,13 @@ for city in df['city_name'].drop_duplicates():
         result = pred.cpu().detach().numpy()[-3:]
         result_dict[industry] = list(result)
     df1 = pd.DataFrame(result_dict,index=['2023-11-28','2023-11-29','2023-11-30'])
-    df1.to_excel(fr'C:\Users\鸽子\Desktop\11月行业电量预测27-30\{city} .xlsx')
+    df1['city_name'] = city
+    df1 = df1[df_city.columns]
+    df1 = pd.concat((df_city.iloc[:27], df1))
+    print(df_city)
+    print(df1)
+    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\行业电量预测v1129.xlsx',mode='a',engine='openpyxl',if_sheet_exists='replace') as writer:
+        df1.to_excel(writer,sheet_name=f'{city[4:6]}')
 print(time.time()-t1)
 print(result_dict)