You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import xgboost as xgb
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.metrics import r2_score
|
|
def normal(x):
|
|
high = x.describe()['75%'] + 1.5*(x.describe()['75%']-x.describe()['25%'])
|
|
low = x.describe()['25%'] - 1.5*(x.describe()['75%']-x.describe()['25%'])
|
|
return x[(x<=high)&(x>=low)]
|
|
|
|
def season(x):
|
|
if str(x)[5:7] in ('04', '05', '06', '11'):
|
|
return 0
|
|
elif str(x)[5:7] in ('01', '02', '03', '09', '10', '12'):
|
|
return 1
|
|
else:
|
|
return 2
|
|
|
|
df = pd.read_excel('./浙江各地市分电压日电量数据/衢州 .xlsx')
|
|
df = df[['stat_date','0.4kv及以下']]
|
|
df['0.4kv及以下'] = df['0.4kv及以下']/10000
|
|
df['stat_date'] = df['stat_date'].map(lambda x:x.strip())
|
|
df['stat_date'] = pd.to_datetime(df['stat_date'])
|
|
|
|
|
|
df_qw = pd.read_excel(r'C:\python-project\p1031\入模数据\衢州.xlsx')
|
|
df_qw.columns = df_qw.columns.map(lambda x:x.strip())
|
|
|
|
df_qw = df_qw[['dtdate','tem_max','tem_min','holiday','24ST']]
|
|
df_qw['dtdate'] = pd.to_datetime(df_qw['dtdate'])
|
|
|
|
|
|
df = pd.merge(df,df_qw,left_on='stat_date',right_on='dtdate',how='left')
|
|
df.drop(columns='dtdate',inplace=True)
|
|
df.set_index('stat_date',inplace=True)
|
|
|
|
|
|
# list2 = []
|
|
# list0 = []
|
|
# list1 = []
|
|
# for i in ('01','02','03','04','05','06','07','08','09','10','11','12'):
|
|
# month_index = df.index.strftime('%Y-%m-%d').str[5:7] == f'{i}'
|
|
# # print(df.loc[month_index]['0.4kv及以下'].max(),df['0.4kv及以下'].describe()['75%'])
|
|
# if df.loc[month_index]['0.4kv及以下'].mean() >= df['0.4kv及以下'].describe()['75%']:
|
|
# list2.append(i)
|
|
# elif df.loc[month_index]['0.4kv及以下'].mean() <= df['0.4kv及以下'].describe()['25%']:
|
|
# list0.append(i)
|
|
# else:
|
|
# list1.append(i)
|
|
# print(list0,list1,list2)
|
|
|
|
|
|
df['season'] = df.index.map(season)
|
|
df = df.loc[normal(df['0.4kv及以下']).index]
|
|
|
|
x_train = df.loc['2021-7':'2023-9'][:-3].drop(columns='0.4kv及以下')
|
|
|
|
y_train = df.loc['2021-7':'2023-9'][:-3]['0.4kv及以下']
|
|
x_eval = df.loc['2023-9'].drop(columns='0.4kv及以下')
|
|
y_eval = df.loc['2023-9']['0.4kv及以下']
|
|
|
|
|
|
x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,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)
|
|
y_pred = model.predict(x_test)
|
|
print(r2_score(y_test,y_pred))
|
|
|
|
predict = model.predict(x_eval)
|
|
result = pd.DataFrame({'eval':y_eval,'pred':predict},index=y_eval.index)
|
|
print(result)
|
|
print((result['eval'][-3:].sum()-result['pred'][-3:].sum())/result['eval'].sum())
|
|
|
|
|
|
|