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76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
11 months ago
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import pandas as pd
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import matplotlib.pyplot as plt
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import r2_score
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def normal(x):
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high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
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low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
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return x[(x<=high)&(x>=low)]
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def season(x):
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if str(x)[5:7] in ('04', '05', '06', '11'):
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return 0
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elif str(x)[5:7] in ('01', '02', '03', '09', '10', '12'):
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return 1
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else:
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return 2
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df = pd.read_excel('./浙江各地市分电压日电量数据/衢州 .xlsx')
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df = df[['stat_date','0.4kv及以下']]
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df['0.4kv及以下'] = df['0.4kv及以下']/10000
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df['stat_date'] = df['stat_date'].map(lambda x:x.strip())
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df['stat_date'] = pd.to_datetime(df['stat_date'])
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df_qw = pd.read_excel(r'C:\python-project\p1031\入模数据\衢州.xlsx')
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df_qw.columns = df_qw.columns.map(lambda x:x.strip())
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df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
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df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
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df = pd.merge(df,df_qw,left_on='stat_date',right_on='dtdate',how='left')
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df.drop(columns='dtdate',inplace=True)
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df.set_index('stat_date',inplace=True)
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# list2 = []
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# list0 = []
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# list1 = []
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# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
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# month_index = df.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
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# # print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
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# if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
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# list2.append(i)
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# elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
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# list0.append(i)
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# else:
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# list1.append(i)
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# print(list0,list1,list2)
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df['season'] = df.index.map(season)
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df = df.loc[normal(df['0.4kv及以下']).index]
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x_train = df.loc['2021-7':'2023-9'][:-3].drop(columns='0.4kv及以下')
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y_train = df.loc['2021-7':'2023-9'][:-3]['0.4kv及以下']
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x_eval = df.loc['2023-9'].drop(columns='0.4kv及以下')
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y_eval = df.loc['2023-9']['0.4kv及以下']
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x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2,random_state=42)
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model = xgb.XGBRegressor(max_depth=6,learning_rate=0.05,n_estimators=150)
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model.fit(x_train,y_train)
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y_pred = model.predict(x_test)
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print(r2_score(y_test,y_pred))
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predict = model.predict(x_eval)
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result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
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print(result)
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print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
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