输出预测结果

各地级市日电量模型/丽水.py

@@ -83,19 +83,20 @@ print(goal2)
 import numpy as np
 
 X_eval = np.array([
-    [11.59, 0.6, 0, 0, 1, 2023],
+    [16.2, 3.2, 0, 0, 1, 2023],
-    [16.6, 2.4, 0, 0, 1, 2023],
+    [17.9, 6.4, 0, 0, 1, 2023],
-    [15.5, 6.7, 0, 0, 1, 2023],
+    [15.7, 9.0, 0, 0, 1, 2023],
-    [15, 9.4, 0, 0, 1, 2023],
+    [19.2, 8.1, 0, 3, 1, 2023],
-    [18.6, 7.5, 0, 1, 1, 2023]
+    [11.8, 2.4, 0, 3, 1, 2023]
 ])
+# X_eval = np.array([[6.2,5.1,0,0,1,2023]])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28','2023-12-29','2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28','2023-12-29','2023-12-30','2023-12-31' ])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx',mode='a',if_sheet_exists='replace',engine='openpyxl') as writer:
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx',mode='a',if_sheet_exists='replace',engine='openpyxl') as writer:
     result.to_excel(writer,sheet_name='丽水')

各地级市日电量模型/台州.py

@@ -82,20 +82,20 @@ print(result_eval)
 import numpy as np
 
 X_eval = np.array([
-    [13.1, -0.7, 0, 0, 1, 2023],
+    [17.3, 2.2, 0, 0, 1, 2023],
-    [17.9, 2.8, 0, 0, 1, 2023],
+    [19, 5.5, 0, 0, 1, 2023],
-    [15.8, 7.6, 0, 0, 1, 2023],
+    [16, 6.5, 0, 0, 1, 2023],
-    [15.9, 8, 0, 0, 1, 2023],
+    [19.7, 7.9, 0, 3, 1, 2023],
-    [19.7, 7.8, 0, 1, 1, 2023]
+    [11.9, 3.2, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='台州')

各地级市日电量模型/嘉兴.py

@@ -81,20 +81,20 @@ loaded_model.load_model('jiaxing.bin')
 import numpy as np
 
 X_eval = np.array([
-    [10.7, -2.2, 0, 0, 1, 2023],
+    [13.8, 2.1, 0, 0, 1, 2023],
-    [12.8, 2.2, 0, 0, 1, 2023],
+    [12.1, 1.7, 0, 0, 1, 2023],
-    [11.7, 2.3, 0, 0, 1, 2023],
+    [10.3, 3.0, 0, 0, 1, 2023],
-    [13, 1, 0, 0, 1, 2023],
+    [12.1, 7.1, 0, 3, 1, 2023],
-    [11.5, 5.8, 0, 1, 1, 2023]
+    [8.2, 1.3, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='嘉兴')

各地级市日电量模型/宁波.py

@@ -81,20 +81,20 @@ import numpy as np
 
 X_eval = np.array([
 
-    [12.9, -2.9, 0, 0, 1, 2023],
+    [16.0, 3.0, 0, 0, 1, 2023],
-    [16.4, 3.4, 0, 0, 1, 2023],
+    [14.8, 5.0, 0, 0, 1, 2023],
-    [12.8, 4.9, 0, 0, 1, 2023],
+    [12.8, 4.8, 0, 0, 1, 2023],
-    [12.9, 4.6, 0, 0, 1, 2023],
+    [20.9, 6.8, 0, 3, 1, 2023],
-    [17.9, 6.4, 0, 1, 1, 2023]
+    [9.5, 2.6, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='宁波')

各地级市日电量模型/杭州.py

@@ -37,7 +37,7 @@ parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
 data = pd.read_excel(os.path.join(parent_dir, '入模数据/杭州.xlsx'))
 data['dtdate'] = pd.to_datetime(data['dtdate'], format='%Y-%m-%d')
 data['year'] = data['dtdate'].dt.year
-# data.index = pd.to_datetime(data.index, format='%Y-%m-%d')
+
 data.set_index('dtdate', inplace=True)
 data = data.loc[normal(data['售电量']).index]
 
@@ -58,7 +58,6 @@ data = data.loc[normal(data['售电量']).index]
 data['season'] = data.index.map(season)
 
 df_train = data[-180:]
-
 df_eval = data.loc['2023-12']
 
 print(df_train)
@@ -87,20 +86,20 @@ print(result_eval)
 print('r2:', r2_score(y_test, y_pred))
 
 X_eval = np.array([
-    [11.8, -2.8, 0, 0, 1, 2023],
+    [14.9, 0.3, 0, 0, 1, 2023],
-    [15.1, 0.2, 0, 0, 1, 2023],
+    [14.3, 1.2, 0, 0, 1, 2023],
-    [12.9, 2.2, 0, 0, 1, 2023],
+    [10.6, 2.7, 0, 0, 1, 2023],
-    [11.4, 1.3, 0, 0, 1, 2023],
+    [17.2, 2.7, 0, 3, 1, 2023],
-    [14.8, 3.6, 0, 1, 1, 2023]
+    [10.7, 0.6, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='杭州')

各地级市日电量模型/温州.py

@@ -83,20 +83,20 @@ print(result_eval)
 import numpy as np
 
 X_eval = np.array([
-    [13.7, -0.1, 0, 0, 1, 2023],
+    [18, 3.3, 0, 0, 1, 2023],
-    [18.9, 3.4, 0, 0, 1, 2023],
+    [18.8, 8.2, 0, 0, 1, 2023],
-    [16.3, 11.3, 0, 0, 1, 2023],
+    [16.9, 11.2, 0, 0, 1, 2023],
-    [16.6, 9.7, 0, 0, 1, 2023],
+    [20.7, 11.6, 0, 3, 1, 2023],
-    [20.6, 20.2, 0, 1, 1, 2023]
+    [14.5, 3.3, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='温州')

各地级市日电量模型/湖州.py

@@ -84,20 +84,20 @@ import numpy as np
 
 X_eval = np.array([
 
-    [9.7, -2.3, 0, 0, 1, 2023],
+    [12.9, 2.1, 0, 0, 1, 2023],
-    [13.1, 0.8, 0, 0, 1, 2023],
+    [11.7, 2.2, 0, 0, 1, 2023],
-    [10.3, 5.8, 0, 0, 1, 2023],
+    [8, 1.9, 0, 0, 1, 2023],
-    [10.1, 0.1, 0, 0, 1, 2023],
+    [13, 4.4, 0, 3, 1, 2023],
-    [12.8, 5.1, 0, 1, 1, 2023]
+    [9.7, 0.7, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='湖州')

各地级市日电量模型/绍兴.py

@@ -80,20 +80,20 @@ import numpy as np
 
 X_eval = np.array([
 
-    [13.1, -3.5, 0, 0, 1, 2023],
+    [15.4, 0.1, 0, 0, 1, 2023],
-    [15.6, 0.1, 0, 0, 1, 2023],
+    [14.2, 3.5, 0, 0, 1, 2023],
-    [13.2, 3.6, 0, 0, 1, 2023],
+    [12.8, 4.6, 0, 0, 1, 2023],
-    [14.4, 3.2, 0, 0, 1, 2023],
+    [19.7, 5.2, 0, 3, 1, 2023],
-    [15.5, 5.3, 0, 1, 1, 2023]
+    [10.9, 0.8, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'],result))
 result.index = result.index.map(lambda x:str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='绍兴')

各地级市日电量模型/舟山.py

@@ -74,20 +74,20 @@ print(result_eval)
 import numpy as np
 
 X_eval = np.array([
-    [11.4, 5.1, 0, 0, 1, 2023],
+    [15.1, 8.2, 0, 0, 1, 2023],
-    [15.5, 9.1, 0, 0, 1, 2023],
+    [12.5, 8.5, 0, 0, 1, 2023],
-    [11, 7.9, 0, 0, 1, 2023],
+    [12, 8.0, 0, 0, 1, 2023],
-    [12.7, 7.2, 0, 0, 1, 2023],
+    [15.0, 10.0, 0, 3, 1, 2023],
-    [16, 10.3, 0, 1, 1, 2023]
+    [9.3, 5.9, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'], result))
 result.index = result.index.map(lambda x: str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='舟山')

各地级市日电量模型/衢州.py

@@ -69,21 +69,21 @@ print(result_eval)
 
 import numpy as np
 X_eval = np.array([
-    [11.7, -0.8, 0, 0, 1, 2023],
+    [15.0, 3.0, 0, 0, 1, 2023],
-    [15.2, 3.0, 0, 0, 1, 2023],
+    [15.7, 5.1, 0, 0, 1, 2023],
-    [14.8, 5.3, 0, 0, 1, 2023],
+    [15.2, 5.6, 0, 0, 1, 2023],
-    [15.1, 5.7, 0, 0, 1, 2023],
+    [17.9, 6.8, 0, 3, 1, 2023],
-    [17.5, 7.5, 0, 1, 1, 2023]
+    [12.0, 2.9, 0, 3, 1, 2023]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'],result))
 result.index = result.index.map(lambda x:str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='衢州')
 

各地级市日电量模型/金华.py

@@ -74,20 +74,20 @@ print(result_eval)
 
 import numpy as np
 X_eval = np.array([
-    [11.7, -0.8, 0, 0, 1],
+    [16.3, 2.5, 0, 0, 1],
-    [15.2, 3.0, 0, 0, 1],
+    [16.9, 3.8, 0, 0, 1],
-    [14.8, 5.3, 0, 0, 1],
+    [16.5, 6.2, 0, 0, 1],
-    [15.1, 5.7, 0, 0, 1],
+    [18.8, 7.8, 0, 3, 1],
-    [17.5, 7.5, 0, 1, 1]
+    [12.5, 2.8, 0, 3, 1]
 ])
 print(model.predict(X_eval))
 result = model.predict(X_eval)
-result = pd.DataFrame(result, index=['2023-12-26', '2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30'])
+result = pd.DataFrame(result, index=['2023-12-27', '2023-12-28', '2023-12-29', '2023-12-30', '2023-12-31'])
 result = pd.concat((result_eval['eval'],result))
 result.index = result.index.map(lambda x:str(x)[:10])
 result.columns = ['预测值']
 print(result)
 
-with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市区日电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                     engine='openpyxl') as writer:
     result.to_excel(writer, sheet_name='金华')

浙江电压等级电量/分压_移动平均.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江power1225.xlsx', sheet_name=1)
+df = pd.read_excel(r'C:\Users\鸽子\Desktop\zj20231228.xlsx', sheet_name=1)
 df['pt_date'] = pd.to_datetime(df['pt_date'])
 
 # 移动平均
@@ -9,7 +9,7 @@ dict_ok = {}
 
 for city in df['city_name'].drop_duplicates():
 
-    df_city1 = df[(df['city_name'] == city) & (df['county_name'].isnull())].set_index('pt_date').loc['2023-12']
+    df_city1 = df[(df['city_name'] == city) & (df['county_name'].isnull())].set_index('pt_date').loc['2023-12'].sort_index()
     resut_df = pd.DataFrame({})
     index_level = []
     tq_list = []
@@ -21,7 +21,7 @@ for city in df['city_name'].drop_duplicates():
 
         # index_level.append(level)
         df_moving_avg = pd.DataFrame(df_city1[level], index=df_city1.index)
-        future = pd.date_range(start='2023-12-26', periods=5, freq='D')
+        future = pd.date_range(start='2023-12-27', periods=5, freq='D')
 
         for date in future:
             df_moving_avg.loc[date, level] = df_moving_avg[-3:].mean().values
@@ -38,7 +38,7 @@ for city in df['city_name'].drop_duplicates():
     #                         index=index_level)
     # resut_df = pd.DataFrame({'预测电量': pred_list},index=index_level)
 
-    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市分压电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市分压电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                         engine='openpyxl') as writer:
         resut_df.to_excel(writer, sheet_name=f'{city[4:6]}')
 

浙江行业电量/行业_移动平均.py

@@ -1,14 +1,13 @@
 import pandas as pd
 
-df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江power1225.xlsx', sheet_name=2)
+df = pd.read_excel(r'C:\Users\鸽子\Desktop\zj20231228.xlsx', sheet_name=2)
 df['stat_date'] = pd.to_datetime(df['stat_date'])
 
 # 移动平均
 for city in df['city_name'].drop_duplicates():
 
     print(city)
-    df_city = df[df['city_name'] == city].set_index('stat_date').loc['2023-12']
+    df_city = df[df['city_name'] == city].set_index('stat_date').loc['2023-12'].sort_index()
-
     dict_big = {}
     dict_ok = {}
     resut_df = pd.DataFrame({})
@@ -21,7 +20,7 @@ for city in df['city_name'].drop_duplicates():
     for industry in df_city.columns[2:]:
         # index_industry.append(industry)
         df_moving_avg = pd.DataFrame(df_city[industry], index=df_city.index)
-        future = pd.date_range(start='2023-12-26', periods=5, freq='D')
+        future = pd.date_range(start='2023-12-27', periods=5, freq='D')
 
         for date in future:
             df_moving_avg.loc[date, industry] = df_moving_avg[-3:].mean().values
@@ -33,7 +32,7 @@ for city in df['city_name'].drop_duplicates():
         # pred_list.append(df_moving_avg[industry].sum())
         # loss_list.append(df_city1[industry].sum()-df_moving_avg[industry].sum())
         # rate_list.append((df_city1[industry].sum()-df_moving_avg[industry].sum())/df_city1[industry].sum())
-    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\行业电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
+    with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\行业电量预测_1228.xlsx', mode='a', if_sheet_exists='replace',
                         engine='openpyxl') as writer:
         resut_df.to_excel(writer, sheet_name=f'{city[4:6]}')