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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) + 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 b0bf3e1..ff2296f 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 @@ -41,7 +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) 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 01dbc3f..c2bcc78 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 @@ -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) 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 f4c6ea2..a2e25f1 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 @@ -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) \ No newline at end of file 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 862c91e..2a90ee5 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 @@ -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) 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 9d8ab27..dd25434 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 @@ -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) \ No newline at end of file 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 6bc0de8..6575bee 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 @@ -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) 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 f218c85..e5fa10e 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 @@ -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) \ No newline at end of file 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 7837aa2..86f2f6d 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 @@ -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) \ No newline at end of file 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 36ca8db..1b8b13b 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 @@ -41,8 +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 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 e674061..4aa2869 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 @@ -41,11 +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) \ No newline at end of file diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 丽水 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 丽水 .xlsx index 10b4de1..1534d2e 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 丽水 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 丽水 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 台州 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 台州 .xlsx index d049e57..752138c 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 台州 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 台州 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 嘉兴 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 嘉兴 .xlsx index 3106d9d..0520a13 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 嘉兴 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 嘉兴 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 宁波 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 宁波 .xlsx index 935b6f4..7d43f1f 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 宁波 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 宁波 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 杭州 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 杭州 .xlsx index cdacaf3..6792ca9 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 杭州 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 杭州 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 温州 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 温州 .xlsx index 8675ecc..6c90f21 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 温州 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 温州 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 湖州 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 湖州 .xlsx index e7e6ad8..b772f87 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 湖州 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 湖州 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 绍兴 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 绍兴 .xlsx index 2deaa7e..32b8db3 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 绍兴 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 绍兴 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 舟山 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 舟山 .xlsx index ac1eb0e..459fdf7 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 舟山 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 舟山 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 衢州 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 衢州 .xlsx index 5d2c300..5fd9a20 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 衢州 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 衢州 .xlsx differ diff --git a/浙江电压等级电量/浙江各地市分电压日电量数据/ 金华 .xlsx b/浙江电压等级电量/浙江各地市分电压日电量数据/ 金华 .xlsx index 6724b4e..ce18782 100644 Binary files a/浙江电压等级电量/浙江各地市分电压日电量数据/ 金华 .xlsx and b/浙江电压等级电量/浙江各地市分电压日电量数据/ 金华 .xlsx differ diff --git a/浙江电压等级电量/电压等级_输入10_输出3.py b/浙江电压等级电量/电压等级_输入10_输出3.py index 1066cd7..ac07c67 100644 --- a/浙江电压等级电量/电压等级_输入10_输出3.py +++ b/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 diff --git a/浙江行业电量/行业电量_输出为3_步长为10.py b/浙江行业电量/行业电量_输出为3_步长为10.py index b0c39a4..0e0420c 100644 --- a/浙江行业电量/行业电量_输出为3_步长为10.py +++ b/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)