From bf4803a140afd6229102a0e7ecbca62a75982cba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=B8=BD=E5=AD=90?= <2316994765@qq.com> Date: Thu, 26 Oct 2023 18:44:34 +0800 Subject: [PATCH] =?UTF-8?q?=E8=A1=A5=E5=85=859=E6=9C=88=E6=95=B0=E6=8D=AE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 各地级市日电量模型/杭州.py | 12 +++--- .../电压等级_输出为3.py | 43 ++++++++++++------- 2 files changed, 34 insertions(+), 21 deletions(-) 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 bf5de92..450296e 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 @@ -37,8 +37,8 @@ def normal(nd): data = pd.read_excel(r'C:\python-project\pytorch3\入模数据\杭州数据.xlsx',index_col='dtdate') data.index = pd.to_datetime(data.index,format='%Y-%m-%d') data = data.loc[normal(data['售电量']).index] -plt.plot(range(len(data['售电量']['2021':'2022'])),data['售电量']['2021':'2022']) -plt.show() +# plt.plot(range(len(data['售电量']['2021':'2022'])),data['售电量']['2021':'2022']) +# plt.show() # print(hf_season(data.loc['2021']['售电量'])) @@ -47,8 +47,8 @@ data['month'] = data['month'].astype('int') data['season'] = data.index.map(season) print(data.head(50)) -df_eval = data.loc['2023-7'] -df_train = data.loc['2021-1':'2023-6'] +df_eval = data.loc['2022-9':'2023-9'] +df_train = data.loc['2021-1':'2022-8'] # df_train = df[500:850] print(len(df_eval),len(df_train),len(data)) @@ -73,7 +73,7 @@ print(y.describe()) # best_i = {} # for i in range(400): -x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.15,random_state=42) +x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42) model = xgb.XGBRegressor(max_depth=6, learning_rate=0.05, n_estimators=150) model.fit(x_train,y_train) @@ -96,6 +96,8 @@ goal2 = (result_eval['eval'][-23:].sum()-result_eval['pred'][-23:].sum())/result print('goal2:',goal2) print(result_eval) print('r2:',r2_score(y_test,y_pred)) + +# result_eval.to_csv('asda.csv',encoding='gbk') # if abs(goal) < best_goal: # best_goal = abs(goal) # best_i['best_i'] = i diff --git a/浙江电压等级电量/电压等级_输出为3.py b/浙江电压等级电量/电压等级_输出为3.py index d8feb5a..06d144c 100644 --- a/浙江电压等级电量/电压等级_输出为3.py +++ b/浙江电压等级电量/电压等级_输出为3.py @@ -38,6 +38,30 @@ def normal(nd): low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%']) return nd[(ndlow)] + + +# def get_data(): +file_dir = r'C:\Users\鸽子\Desktop\浙江各地市分电压日电量数据' +dataset_x = [] +for excel in os.listdir(file_dir): + data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col=' stat_date ') + data.columns = data.columns.map(lambda x: x.strip()) + + data.index = pd.to_datetime(data.index,format='%Y%m%d') + data.sort_index(inplace=True) + + data = data.loc['2021-01':'2023-08'] + data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True) # 去除0值列 + print('len(data):', len(data)) + list_app = [] + for level in data.columns: + df = data[level] + df = df[df.values != 0] # 去除0值行 + df = normal(df) + df = df.astype('float32').values # 转换数据类型 + dataset_x create_dataset(df,DAYS_FOR_TRAIN) + + def run(file_dir,excel): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -53,8 +77,8 @@ def run(file_dir,excel): data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True) # 去除0值列 print('len(data):', len(data)) list_app = [] - for industry in data.columns: - df = data[industry] + for level in data.columns: + df = data[level] df = df[df.values != 0] # 去除0值行 df = normal(df) df = df.astype('float32').values # 转换数据类型 @@ -124,14 +148,6 @@ def run(file_dir,excel): pred = model(x.reshape(-1,1,DAYS_FOR_TRAIN)).view(-1).detach().numpy() - # for i in range(3): - # next_1_8 = x[1:] - # next_9 = model(x.reshape(-1,1,DAYS_FOR_TRAIN)) - # # print(next_9,next_1_8) - # x = torch.concatenate((next_1_8, next_9.view(-1))) - # result_list.append(next_9.view(-1).item()) - - # 反归一化 pred = pred * (max_value - min_value) + min_value df = df * (max_value - min_value) + min_value @@ -155,11 +171,6 @@ def run(file_dir,excel): # f.write(f'{excel[:2]}{industry}:{round(target, 5)}\n') if __name__ == '__main__': - file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据' + file_dir = r'C:\Users\鸽子\Desktop\浙江各地市分电压日电量数据' run(file_dir,'杭州.xlsx') - # p = Pool(4) - # for excel in os.listdir(file_dir): - # p.apply_async(func=run,args=(file_dir,excel)) - # p.close() - # p.join() \ No newline at end of file