首页 > 其他分享> > 【第十届“泰迪杯”数据挖掘挑战赛】B题：电力系统负荷预测分析 ARIMA、AutoARIMA、LSTM、Prophet、多元Prophet 实现

【第十届“泰迪杯”数据挖掘挑战赛】B题：电力系统负荷预测分析 ARIMA、AutoARIMA、LSTM、Prophet、多元Prophet 实现

2022-03-18 17:31:32 作者：互联网

完整代码下载链接

1 读取数据预处理的文件

import numpy as np
import pandas as pd

import seaborn as sns 
import matplotlib.pyplot as plt 
from colorama import Fore

from sklearn.metrics import mean_absolute_error, mean_squared_error
import math

import warnings # Supress warnings 
warnings.filterwarnings('ignore')
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
np.random.seed(7)

df = pd.read_csv(r"./data/泰迪杯数据2.csv")
df.head()

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df  = df.rename(columns={'日期1':'date'})
df

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2 查看时序

from datetime import datetime, date 

df['date'] = pd.to_datetime(df['date'])
df.head().style.set_properties(subset=['date'], **{'background-color': 'dodgerblue'})

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# To compelte the data, as naive method, we will use ffill
f, ax = plt.subplots(nrows=7, ncols=1, figsize=(15, 25))


for i, column in enumerate(df.drop('date', axis=1).columns):
  。。。略

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df = df.sort_values(by='date')

# Check time intervals
df['delta'] = df['date'] - df['date'].shift(1)

df[['date', 'delta']].head()

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df['delta'].sum(), df['delta'].count()

(Timedelta(‘13 days 23:45:00’), 1439)

df = df.drop('delta', axis=1)
df.isna().sum()

date 0
总有功功率（kw） 51
最高温度 6
最低温度 0
白天风力风向 0
夜晚风力风向 0
天气1 0
天气2 0
dtype: int64

3 异常值缺失值

f, ax = plt.subplots(nrows=2, ncols=1, figsize=(15, 15))

。。。略


ax[1].set_xlim([date(2018, 1, 1), date(2018, 1, 15)])

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3.1 HeatMap颜色

Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r,

BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r,

Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r,

Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r,

PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r,

RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r,

Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu,

YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary,

binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm,

coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r,

gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow,

gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2,

gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno,

inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r,

ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r,

rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r,

tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r,

twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(16,5))

sns.heatmap(df.T.isna(), cmap='Reds_r')
ax.set_title('Missing Values', fontsize=16)

for tick in ax.yaxis.get_major_ticks():
    tick.label.set_fontsize(14)
plt.show()

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3.2 缺失值处理（多种填充方式）

f, ax = plt.subplots(nrows=4, ncols=1, figsize=(15, 12))

sns.lineplot(x=df['date'], y=df['总有功功率（kw）'].fillna(0), ax=ax[0], color='darkorange', label = 'modified')
sns.lineplot(x=df['date'], y=df['总有功功率（kw）'].fillna(np.inf), ax=ax[0], color='dodgerblue', label = 'original')
ax[0].set_title('Fill NaN with 0', fontsize=14)
ax[0].set_ylabel(ylabel='Volume', fontsize=14)

。。。略

for i in range(4):
    ax[i].set_xlim([date(2018, 1, 1), date(2018, 1, 15)])
    
plt.tight_layout()
plt.show()

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df['总有功功率（kw）'] = df['总有功功率（kw）'].interpolate()

4 数据平滑与采样

重采样可以提供数据的附加信息。有两种类型的重采样:

上采样是指增加采样频率(例如从几天到几小时)

下采样是指降低采样频率(例如，从几天到几周)

在这个例子中，我们将使用。resample()函数

fig, ax = plt.subplots(ncols=1, nrows=3, sharex=True, figsize=(16,12))

sns.lineplot(df['date'], df['总有功功率（kw）'], color='dodgerblue', ax=ax[0])
ax[0].set_title('总有功功率（kw） Volume', fontsize=14)

。。。略
for i in range(3):
    ax[i].set_xlim([date(2018, 1, 1), date(2018, 1, 14)])

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# As we can see, downsample to weekly could smooth the data and hgelp with analysis
downsample = df[['date',
                 '总有功功率（kw）', 
                ]].resample('7D', on='date').mean().reset_index(drop=False)

# df = downsample.copy()
downsample

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5 平稳性检验

目测:绘制时间序列并检查趋势或季节性

基本统计:分割时间序列并比较每个分区的平均值和方差

统计检验:增强的迪基富勒检验

# A year has 52 weeks (52 weeks * 7 days per week) aporx.
rolling_window = 52
f, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 6))

。。。略
plt.show()

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现在，我们将检查每个变量: p值小于0.05 检查ADF统计值与critical_values的比较范围

from statsmodels.tsa.stattools import adfuller

result = adfuller(df['总有功功率（kw）'].values)
result

(-5.279986646245767, 6.0232754503160645e-06, 24, 1415,

{‘1%’: -3.434979825137732, ‘5%’: -2.8635847436211317, ‘10%’: -2.5678586114197954}, 29608.16365155926)

# Thanks to https://www.kaggle.com/iamleonie for this function!
f, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 6))

def visualize_adfuller_results(series, title, ax):
   。。。略

visualize_adfuller_results(df['总有功功率（kw）'].values, '总有功功率（kw）',ax=ax)
# visualize_adfuller_results(df['temperature'].values, 'Temperature', ax[1, 0])
# visualize_adfuller_results(df['river_hydrometry'].values, 'River_Hydrometry', ax[0, 1])
# visualize_adfuller_results(df['drainage_volume'].values, 'Drainage_Volume', ax[1, 1])
# visualize_adfuller_results(df['depth_to_groundwater'].values, 'Depth_to_Groundwater', ax[2, 0])

# f.delaxes(ax[2, 1])
plt.tight_layout()
plt.show()

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如果数据不是静态的，但我们想使用一个模型，如ARIMA(需要这个特征)，数据必须转换。

将序列转换为平稳序列的两种最常见的方法是:

变换:例如对数或平方根，以稳定非恒定方差

差分:从以前的值中减去当前值

6 数据转换

（1）对数

df['总有功功率（kw）_log'] = np.log(abs(df['总有功功率（kw）']))

。。。略
sns.distplot(df['总有功功率（kw）_log'], ax=ax[1])

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（2）一阶差分

# First Order Differencing
ts_diff = np.diff(df['总有功功率（kw）'])
df['总有功功率（kw）_diff_1'] = np.append([0], ts_diff)

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 6))
visualize_adfuller_results(df['总有功功率（kw）_diff_1'], 'Differenced (1. Order) \n Depth to Groundwater', ax)

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7 特征工程

7.1 时序提取

df['year'] = pd.DatetimeIndex(df['date']).year
df['month'] = pd.DatetimeIndex(df['date']).month
df['day'] = pd.DatetimeIndex(df['date']).day
。。。略

df[['date', 'year', 'month', 'day', 'day_of_year', 'week_of_year', 'quarter', 'season']].head()

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7.2 编码循环特征

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 3))

sns.lineplot(x=df['date'], y=df['month'], color='dodgerblue')
ax.set_xlim([date(2018, 1, 1), date(2018, 1, 14)])
plt.show()

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month_in_year = 12
df['month_sin'] = np.sin(2*np.pi*df['month']/month_in_year)
df['month_cos'] = np.cos(2*np.pi*df['month']/month_in_year)

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))

sns.scatterplot(x=df.month_sin, y=df.month_cos, color='dodgerblue')
plt.show()

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7.3 时间序列分解

from statsmodels.tsa.seasonal import seasonal_decompose

core_columns =  [
    '总有功功率（kw）']
。。。略

fig, ax = plt.subplots(ncols=2, nrows=4, sharex=True, figsize=(16,8))

for i, column in enumerate(['总有功功率（kw）', '最低温度']):
    
    res = seasonal_decompose(df[column], freq=52, model='additive', extrapolate_trend='freq')

    ax[0,i].set_title('Decomposition of {}'.format(column), fontsize=16)
    res.observed.plot(ax=ax[0,i], legend=False, color='dodgerblue')
    ax[0,i].set_ylabel('Observed', fontsize=14)
。。。略

plt.show()

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7.4 滞后特征

weeks_in_month = 4

for column in core_columns:
    df[f'{column}_seasonal_shift_b_2m'] = df[f'{column}_seasonal'].shift(-2 * weeks_in_month)
    df[f'{column}_seasonal_shift_b_1m'] = df[f'{column}_seasonal'].shift(-1 * weeks_in_month)
    df[f'{column}_seasonal_shift_1m'] = df[f'{column}_seasonal'].shift(1 * weeks_in_month)
    df[f'{column}_seasonal_shift_2m'] = df[f'{column}_seasonal'].shift(2 * weeks_in_month)
    df[f'{column}_seasonal_shift_3m'] = df[f'{column}_seasonal'].shift(3 * weeks_in_month)

7.6 探索性数据分析

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 6))
f.suptitle('Seasonal Components of Features', fontsize=16)

for i, column in enumerate(core_columns):
    。。。略
    
plt.tight_layout()
plt.show()

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7.7 相关性分析

f, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))

。。。略

plt.tight_layout()
plt.show()

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7.8 自相关分析

from pandas.plotting import autocorrelation_plot

autocorrelation_plot(df['总有功功率（kw）_diff_1'])
plt.show()

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from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf

f, ax = plt.subplots(nrows=2, ncols=1, figsize=(16, 8))
。。。略

plt.show()

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8 建模

8.1 时序中交叉验证

from sklearn.model_selection import TimeSeriesSplit

N_SPLITS = 3

X = df['date']
y = df['总有功功率（kw）']

folds = TimeSeriesSplit(n_splits=N_SPLITS)

f, ax = plt.subplots(nrows=N_SPLITS, ncols=2, figsize=(16, 9))

for i, (train_index, valid_index) in enumerate(folds.split(X)):
    。。。略
for i in range(N_SPLITS):
    ax[i, 0].set_xlim([date(2018, 1, 1), date(2018, 1, 14)])
    ax[i, 1].set_xlim([date(2018, 1, 1), date(2018, 6, 30)])
    
plt.tight_layout()
plt.show()

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8.2 单变量时间序列模型

train_size = int(0.85 * len(df))
test_size = len(df) - train_size
df = df.fillna(0)
univariate_df = df[['date', '总有功功率（kw）']].copy()
univariate_df.columns = ['ds', 'y']

train = univariate_df.iloc[:train_size, :]

x_train, y_train = pd.DataFrame(univariate_df.iloc[:train_size, 0]), pd.DataFrame(univariate_df.iloc[:train_size, 1])
x_valid, y_valid = pd.DataFrame(univariate_df.iloc[train_size:, 0]), pd.DataFrame(univariate_df.iloc[train_size:, 1])

print(len(train), len(x_valid))

8.2.1 ARIMA

from statsmodels.tsa.arima_model import ARIMA
import warnings
warnings.ignore=True


。。。略

# Prediction with ARIMA
# y_pred, se, conf = model_fit.forecast(202)
y_pred, se, conf = model_fit.forecast(216)

# Calcuate metrics
score_mae = mean_absolute_error(y_valid, y_pred)
score_rmse = math.sqrt(mean_squared_error(y_valid, y_pred))

print(Fore.GREEN + 'RMSE: {}'.format(score_rmse))

RMSE: 30973.353510293528

f, ax = plt.subplots(1)
f.set_figheight(6)
f.set_figwidth(15)

model_fit.plot_predict(1, 1300, ax=ax)
sns.lineplot(x=x_valid.index, y=y_valid['y'], ax=ax, color='orange', label='Ground truth') #navajowhite

ax.set_title(f'Prediction \n MAE: {score_mae:.2f}, RMSE: {score_rmse:.2f}', fontsize=14)
ax.set_xlabel(xlabel='Date', fontsize=14)
ax.set_ylabel(ylabel='总有功功率（kw）', fontsize=14)

ax.set_ylim(100000, 350392)
plt.show()

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f, ax = plt.subplots(1)
f.set_figheight(4)
f.set_figwidth(15)

sns.lineplot(x=x_valid.index, y=y_pred, ax=ax, color='blue', label='predicted') #navajowhite
sns.lineplot(x=x_valid.index, y=y_valid['y'], ax=ax, color='orange', label='Ground truth') #navajowhite

ax.set_xlabel(xlabel='Date', fontsize=14)
ax.set_ylabel(ylabel='总有功功率（kw）', fontsize=14)

plt.show()

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8.2.2 LSTM

from sklearn.preprocessing import MinMaxScaler

data = univariate_df.filter(['y'])
#Convert the dataframe to a numpy array
dataset = data.values

scaler = MinMaxScaler(feature_range=(-1, 0))
scaled_data = scaler.fit_transform(dataset)

scaled_data[:10]

array([[-0.50891613], [-0.50891613], [-0.59567808], [-0.59567808], [-0.60361527], [-1. ], [-0.63509216], [-0.63509216], [-0.58983584], [-0.58983584]])

# Defines the rolling window
look_back = 52
# Split into train and test sets
train, test = scaled_data[:train_size-look_back,:], scaled_data[train_size-look_back:,:]

d。。。略
x_train, y_train = create_dataset(train, look_back)
x_test, y_test = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
x_train = np.reshape(x_train, (x_train.shape[0], 1, x_train.shape[1]))
x_test = np.reshape(x_test, (x_test.shape[0], 1, x_test.shape[1]))

print(len(x_train), len(x_test))

from keras.models import Sequential
from keras.layers import Dense, LSTM

#Build the LSTM model
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(LSTM(64, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')

#Train the model
model.fit(x_train, y_train, batch_size=16, epochs=10, validation_data=(x_test, y_test))

model.summary()

Epoch 1/10 70/70 [] - 15s 10ms/step - loss: 0.0417 - val_loss: 0.0071 Epoch 2/10 70/70 [] - 0s 3ms/step - loss: 0.0104 - val_loss: 0.0036 Epoch 3/10 70/70 [] - 0s 6ms/step - loss: 0.0081 - val_loss: 0.0023 Epoch 4/10 70/70 [] - 0s 4ms/step - loss: 0.0064 - val_loss: 0.0017 Epoch 5/10 70/70 [] - 0s 4ms/step - loss: 0.0059 - val_loss: 0.0017 Epoch 6/10 70/70 [] - 0s 3ms/step - loss: 0.0053 - val_loss: 0.0019 Epoch 7/10 70/70 [] - 0s 3ms/step - loss: 0.0065 - val_loss: 0.0019 Epoch 8/10 70/70 [] - 0s 3ms/step - loss: 0.0051 - val_loss: 0.0013 Epoch 9/10 70/70 [] - 0s 3ms/step - loss: 0.0048 - val_loss: 0.0023 Epoch 10/10 70/70 [] - 0s 4ms/step - loss: 0.0052 - val_loss: 0.0012 Model: “sequential” _________________________________________________________________

Layer (type) Output Shape Param # =================================================================

lstm (LSTM) (None, 1, 128) 92672

stm_1 (LSTM) (None, 64) 49408

dense (Dense) (None, 25) 1625

dense_1 (Dense) (None, 1) 26 =================================================================

Total params: 143,731 Trainable params: 143,731 Non-trainable params: 0

# Lets predict with the model
train_predict = model.predict(x_train)
test_predict = model.predict(x_test)

# invert predictions
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])

test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])

# Get the root mean squared error (RMSE) and MAE
score_rmse = np.sqrt(mean_squared_error(y_test[0], test_predict[:,0]))
score_mae = mean_absolute_error(y_test[0], test_predict[:,0])
print(Fore.GREEN + 'RMSE: {}'.format(score_rmse))
from sklearn.metrics import r2_score
print('R2-score:',r2_score(y_test[0], test_predict[:,0]))

RMSE: 4502.091881948914

R2-score: 0.9519027039841994

x_train_ticks = univariate_df.head(train_size)['ds']
y_train = univariate_df.head(train_size)['y']
x_test_ticks = univariate_df.tail(test_size)['ds']

# Plot the forecast
f, ax = plt.subplots(1)
f.set_figheight(6)
f.set_figwidth(15)

sns.lineplot(x=x_train_ticks, y=y_train, ax=ax, label='Train Set') #navajowhite
sns.lineplot(x=x_test_ticks, y=test_predict[:,0], ax=ax, color='green', label='Prediction') #navajowhite
sns.lineplot(x=x_test_ticks, y=y_test[0], ax=ax, color='orange', label='Ground truth') #navajowhite

ax.set_title(f'Prediction \n MAE: {score_mae:.2f}, RMSE: {score_rmse:.2f}', fontsize=14)
ax.set_xlabel(xlabel='Date', fontsize=14)
ax.set_ylabel(ylabel='总有功功率（kw）', fontsize=14)

plt.show()

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8.2.3 AutoARIMA

from statsmodels.tsa.arima_model import ARIMA
import pmdarima as pm
。。。略
print(model.summary())

在这里插入图片描述

y_pred = model.predict(216)
from sklearn.metrics import r2_score
print('R2-score:',r2_score(y_valid, y_pred))

R2-score: -0.08425358340633804

model.plot_diagnostics(figsize=(16,8))
plt.show()

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8.3 多元时序预测

df.columns

Index([‘date’, ‘总有功功率（kw）’, ‘最高温度’, ‘最低温度’, ‘白天风力风向’, ‘夜晚风力风向’, ‘天气1’, ‘天气2’], dtype=‘object’)

feature_columns = [
     '最高温度', '最低温度', '白天风力风向', '夜晚风力风向', '天气1', '天气2'
]
target_column = ['总有功功率（kw）']

train_size = int(0.85 * len(df))

multivariate_df = df[['date'] + target_column + feature_columns].copy()
multivariate_df.columns = ['ds', 'y'] + feature_columns

train = multivariate_df.iloc[:train_size, :]
x_train, y_train = pd.DataFrame(multivariate_df.iloc[:train_size, [0,2,3,4,5,6,7]]), pd.DataFrame(multivariate_df.iloc[:train_size, 1])
x_valid, y_valid = pd.DataFrame(multivariate_df.iloc[train_size:, [0,2,3,4,5,6,7]]), pd.DataFrame(multivariate_df.iloc[train_size:, 1])

train.head()

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train  =multivariate_df.iloc[:train_size, :]
train

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8.3.1 多元Propher

from fbprophet import Prophet


# Train the model
model = Prophet()
# model.add_regressor('最高温度')
# model.add_regressor('最低温度')
# model.add_regressor('白天风力风向')
# model.add_regressor('夜晚风力风向')
# model.add_regressor('天气1')
# model.add_regressor('天气2')
# Fit the model with train set
model.fit(train)

# Predict on valid set
y_pred = model.predict(x_valid)

# Calcuate metrics
score_mae = mean_absolute_error(y_valid, y_pred['yhat'])
score_rmse = math.sqrt(mean_squared_error(y_valid, y_pred['yhat']))

print(Fore.GREEN + 'RMSE: {}'.format(score_rmse))

from sklearn.metrics import r2_score
print('R2-score:',r2_score(y_valid, y_pred['yhat']))

# Plot the forecast
f, ax = plt.subplots(1)
f.set_figheight(6)
f.set_figwidth(15)

model.plot(y_pred, ax=ax)
sns.lineplot(x=x_valid['ds'], y=y_valid['y'], ax=ax, color='orange', label='Ground truth') #navajowhite

ax.set_title(f'Prediction \n MAE: {score_mae:.2f}, RMSE: {score_rmse:.2f}', fontsize=14)
ax.set_xlabel(xlabel='Date', fontsize=14)
ax.set_ylabel(ylabel='总有功功率（kw）', fontsize=14)

plt.show()

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标签：泰迪杯,plt,set,df,Prophet,date,train,数据挖掘,ax
来源： https://blog.csdn.net/weixin_43935696/article/details/123580248

【第十届“泰迪杯”数据挖掘挑战赛】B题：电力系统负荷预测分析 ARIMA、AutoARIMA、LSTM、Prophet、多元Prophet 实现

目录

相关链接

完整代码下载链接

1 读取数据预处理的文件

2 查看时序

3 异常值缺失值

3.1 HeatMap颜色

3.2 缺失值处理（多种填充方式）

4 数据平滑与采样

5 平稳性检验

6 数据转换

7 特征工程

7.1 时序提取

7.2 编码循环特征

7.3 时间序列分解

7.4 滞后特征

7.6 探索性数据分析

7.7 相关性分析

7.8 自相关分析

8 建模

8.1 时序中交叉验证

8.2 单变量时间序列模型

8.2.1 ARIMA

8.2.2 LSTM

8.2.3 AutoARIMA

8.3 多元时序预测

8.3.1 多元Propher