本项目使用人工神经网络完成蒸汽量回归预测，包括数据处理、异常值处理、相关性分析、模型构建、模型训练、模型预测等步骤。
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工业蒸汽量预测
1. 引言
1.1 项目介绍
- 火力发电的基本原理是：燃料在燃烧时加热水生成蒸汽，蒸汽压力推动汽轮机旋转，然后汽轮机带动发电机旋转，产生电能。在这一系列的能量转化中，影响发电效率的核心是锅炉的燃烧效率，即燃料燃烧加热水产生高温高压蒸汽。锅炉的燃烧效率的影响因素很多，包括锅炉的可调参数，如燃烧给量，一二次风，引风，返料风，给水水量；以及锅炉的工况，比如锅炉床温、床压，炉膛温度、压力，过热器的温度等。
- 本项目使用人工神经网络完成蒸汽量回归预测，包括数据处理、异常值处理、相关性分析、模型构建、模型训练、模型预测等步骤。
1.2 数据集介绍
经脱敏后的锅炉传感器采集的数据（采集频率是分钟级别），根据锅炉的工况，预测产生的蒸汽量。
数据集各个字段以及数据类型如下所示：
- 数据集特征较多，需要筛选有用特征
- 数据集最后一列为需要预测的目标值
- 除最后一列外的数据列都是特征
| V0 | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V36 | V37 | target |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| float | float | float | float | float | float | float | float | float | float | float | float | float | float |
2. 环境准备
2.1 安装环境
In [ ]
pip install missingno -q
Note: you may need to restart the kernel to use updated packages.
2.2 导入所需模块
In [1]
import numpy as np import pandas as pd import matplotlib.pyplot as pltimport seaborn as snsimport missingno as msnofrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.pipeline import Pipelinefrom sklearn.linear_model import LogisticRegressionfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import GradientBoostingClassifierfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCVfrom sklearn.model_selection import cross_val_scorefrom sklearn.metrics import confusion_matrix, classification_report, accuracy_scorefrom sklearn import metricsfrom sklearn.metrics import roc_curve, auc, roc_auc_score
3. 数据处理
3.1 读取数据
In [2]
IndustrialSteam_train=pd.read_csv(r'data/data178496/zhengqi_train.txt',sep='\t') IndustrialSteam_test=pd.read_csv(r'data/data178496/zhengqi_test.txt',sep='\t')
In [ ]
IndustrialSteam_train.head()
V0 V1 V2 V3 V4 V5 V6 V7 V8 V9 ... \
0 0.566 0.016 -0.143 0.407 0.452 -0.901 -1.812 -2.360 -0.436 -2.114 ...
1 0.968 0.437 0.066 0.566 0.194 -0.893 -1.566 -2.360 0.332 -2.114 ...
2 1.013 0.568 0.235 0.370 0.112 -0.797 -1.367 -2.360 0.396 -2.114 ...
3 0.733 0.368 0.283 0.165 0.599 -0.679 -1.200 -2.086 0.403 -2.114 ...
4 0.684 0.638 0.260 0.209 0.337 -0.454 -1.073 -2.086 0.314 -2.114 ...
V29 V30 V31 V32 V33 V34 V35 V36 V37 target
0 0.136 0.109 -0.615 0.327 -4.627 -4.789 -5.101 -2.608 -3.508 0.175
1 -0.128 0.124124 0.032 0.600 -0.843 0.160 0.364 -0.335 -0.730 0.676
2 -0.009 0.361 0.277 -0.116 -0.843 0.160 0.364 0.765 -0.589 0.633
3 0.015 0.417 0.279 0.603 -0.843 -0.065 0.364 0.333 -0.112 0.206
4 0.183 1.078 0.328 0.418 -0.843 -0.215 0.364 -0.280 -0.028 0.384
[5 rows x 39 columns]
3.2 数据分析及可视化
3.2.1 缺失值检测
- 所有属性中有2888个非空值，因此没有丢失值。
- 所有功能的数据类型都是float64，可以传入模型，无需进行数据类型转换。
In [ ]
IndustrialSteam_train.info()
3.2.2 缺失值可视化
In [ ]
missing_values = msno.bar(IndustrialSteam_train, figsize = (16,5),color = "#483D8B")
<Figure size 1600x500 with 3 Axes>
3.2.3 描述统计
In [ ]
IndustrialSteam_train.describe().T
count mean std min 25% 50% 75% max V0 2888.0 0.123048 0.928031 -4.335 -0.29700 0.3590 0.72600 2.121 V1 2888.0 0.056068 0.941515 -5.122 -0.22625 0.2725 0.59900 1.918 V2 2888.0 0.289720 0.911236 -3.420 -0.31300 0.3860 0.91825 2.828 V3 2888.0 -0.067790 0.970298 -3.956 -0.65225 -0.0445 0.62400 2.457 V4 2888.0 0.012921 0.888377 -4.742 -0.38500 0.1100 0.55025 2.689 V5 2888.0 -0.558565 0.517957 -2.182 -0.85300 -0.4660 -0.15400 0.489 V6 2888.0 0.182892 0.918054 -4.576 -0.31000 0.3880 0.83125 1.895 V7 2888.0 0.116155 0.955116 -5.048 -0.29500 0.3440 0.78225 1.918 V8 2888.0 0.177856 0.895444 -4.692 -0.15900 0.3620 0.72600 2.245 V9 2888.0 -0.169452 0.953813 -12.891 -0.39000 0.0420 0.04200 1.335 V10 2888.0 0.034319 0.968272 -2.584 -0.42050 0.1570 0.61925 4.830 V11 2888.0 -0.364465 0.858504 -3.160 -0.80325 -0.1120 0.24700 1.455 V12 2888.0 0.023177 0.894092 -5.165 -0.41900 0.1230 0.61600 2.657 V13 2888.0 0.195738 0.922757 -3.675 -0.39800 0.2895 0.86425 2.475 V14 2888.0 0.016081 1.015585 -2.455 -0.66800 -0.1610 0.82975 2.558 V15 2888.0 0.096146 1.033048 -2.903 -0.66225 -0.0005 0.73000 4.314 V16 2888.0 0.113505 0.983128 -5.981 -0.30000 0.3060 0.77425 2.861 V17 2888.0 -0.043458 0.655857 -2.224 -0.36600 0.1650 0.43000 2.023 V18 2888.0 0.055034 0.953466 -3.582 -0.36750 0.0820 0.51325 4.441 V19 2888.0 -0.114884 1.108859 -3.704 -0.98750 -0.0005 0.73725 3.431 V20 2888.0 -0.186226 0.788511 -3.402 -0.67550 -0.1565 0.30400 3.525 V21 2888.0 -0.056556 0.781471 -2.643 -0.51700 -0.0565 0.43150 2.259 V22 2888.0 0.302893 0.639186 -1.375 -0.06300 0.2165 0.87200 2.018 V23 2888.0 0.155978 0.978757 -5.542 0.09725 0.3380 0.36825 1.906 V24 2888.0 -0.021813 1.033403 -1.344 -1.19100 0.0950 0.93125 2.423 V25 2888.0 -0.051679 0.915957 -3.808 -0.55725 -0.0760 0.35600 7.284 V26 2888.0 0.072092 0.889771 -5.131 -0.45200 0.0750 0.64425 2.980 V27 2888.0 0.272407 0.270374 -1.164 0.15775 0.3250 0.44200 0.925 V28 2888.0 0.137712 0.929899 -2.435 -0.45500 -0.4470 0.73000 4.671 V29 2888.0 0.097648 1.061200 -2.912 -0.66400 -0.0230 0.74525 4.580 V30 2888.0 0.055477 0.901934 -4.507 -0.28300 0.0535 0.48800 2.689 V31 2888.0 0.127791 0.873028 -5.859 -0.17025 0.2995 0.63500 2.013 V32 2888.0 0.020806 0.902584 -4.053 -0.40725 0.0390 0.55700 2.395 V33 2888.0 0.007801 1.006995 -4.627 -0.49900 -0.0400 0.46200 5.465 V34 2888.0 0.006715 1.003291 -4.789 -0.29000 0.1600 0.27300 5.110 V35 2888.0 0.197764 0.985675 -5.695 -0.20250 0.3640 0.60200 2.324 V36 2888.0 0.030658 0.970812 -2.608 -0.41300 0.1370 0.64425 5.238 V37 2888.0 -0.130330 1.017196 -3.630 -0.79825 -0.1855 0.49525 3.000 target 2888.0 0.126353 0.983966 -3.044 -0.35025 0.3130 0.79325 2.538
3.2.4 样本特征分布可视化
In [ ]
hist_plot = IndustrialSteam_train.hist(figsize = (20,20), color = "#483D8B")
<Figure size 2000x2000 with 42 Axes>
3.2.5 相关性分析
- 用于查看特征与特征之间的相关性
- 查看目标值与特征之间的相关性
- 筛选出与目标值有较强相关性的特征
In [ ]
from pylab import mplfrom matplotlib.font_manager import FontProperties myfont=FontProperties(fname=r'/usr/share/fonts/fangzheng/FZSYJW.TTF',size=12) sns.set(font=myfont.get_name()) corr = IndustrialSteam_train.corr()# 调用热力图绘制相关性关系plt.figure(figsize=(25,25),dpi=150) sns.heatmap(corr, square=True, linewidths=0.1, annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7fe003667410>
<Figure size 3750x3750 with 2 Axes>
3.2.6 筛选特征
以0.5为界限，同时在训练集和测试集中去除相关系数绝对值低于0.5的特征，确保被输入模型进行训练的特征与预测目标值有较强的相关性。
In [3]
df_train = IndustrialSteam_train[['V0','V1','V3','V4','V8','V12','V16','V31','target']] df_test = IndustrialSteam_test[['V0','V1','V3','V4','V8','V12','V16','V31']]
3.3 异常值处理
3.3.1 异常值检测
- 使用箱型图查看离群点（Outlier）
- 对于离群点考虑使用异常值处理方法
In [ ]
plt.figure(figsize=(20,10)) sns.boxenplot(data = df_train) plt.xticks(rotation=60) plt.show()
<Figure size 2000x1000 with 1 Axes>
3.3.2 异常值插补
- 与缺失值的插补一样，我们也可以插补异常值。
- 我们可以在这种方法中使用均值、中值和零值来插补。
- 由于我们进行了填补，而不是直接删除异常值。因此不会丢失数据。
- 这里的中值是合适的，因为它不受异常值的影响。
In [ ]
#median imputationimport pandas as pdimport numpy as np
train = df_train
sns.boxplot(train['V0'])
plt.title("Box Plot before median imputation")
plt.show()
q1 = train['V0'].quantile(0.25)
q3 = train['V0'].quantile(0.75)
iqr = q3-q1
Lower_tail = q1 - 1.5 * iqr
Upper_tail = q3 + 1.5 * iqr# V0med = np.median(train['V0'])for i in train['V0']: if i > Upper_tail or i < Lower_tail:
train['V0'] = train['V0'].replace(i, med)
sns.boxplot(train['V0'])
plt.title("Box Plot after median imputation")
plt.show()
# V1med = np.median(train['V1'])for i in train['V1']: if i > Upper_tail or i < Lower_tail:
train['V1'] = train['V1'].replace(i, med)# V3med = np.median(train['V3'])for i in train['V3']: if i > Upper_tail or i < Lower_tail:
train['V3'] = train['V3'].replace(i, med)# V4med = np.median(train['V4'])for i in train['V4']: if i > Upper_tail or i < Lower_tail:
train['V4'] = train['V4'].replace(i, med)# V8med = np.median(train['V8'])for i in train['V8']: if i > Upper_tail or i < Lower_tail:
train['V8'] = train['V8'].replace(i, med)# V12med = np.median(train['V12'])for i in train['V12']: if i > Upper_tail or i < Lower_tail:
train['V12'] = train['V12'].replace(i, med)# V16med = np.median(train['V16'])for i in train['V16']: if i > Upper_tail or i < Lower_tail:
train['V16'] = train['V16'].replace(i, med)# V31med = np.median(train['V31'])for i in train['V31']: if i > Upper_tail or i < Lower_tail:
train['V31'] = train['V31'].replace(i, med)
<Figure size 640x480 with 1 Axes>
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
<Figure size 640x480 with 1 Axes>
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:33: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:39: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:51: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ipykernel_launcher.py:63: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
查看异常值插补后的数据分布
In [ ]
plt.figure(figsize=(20,10)) sns.boxenplot(data = df_train) plt.xticks(rotation=60) plt.show()
<Figure size 2000x1000 with 1 Axes>
3.4 数据预处理
3.4.1 拆分特征数据与目标值数据
- 选择'V0','V1','V3','V4','V8','V12','V16','V31'字段作为特征
- 选择'target'字段作为目标值
In [4]
data_features = df_train.loc[df_train.index[:], ['V0','V1','V3','V4','V8','V12','V16','V31']] data_label = df_train['target']
3.4.2 划分数据集
- 按照0.8：0.2的比例划分训练集与测试集
In [5]
from sklearn.model_selection import train_test_split# 数据集划分x_train, x_test, y_train, y_test = train_test_split(data_features, data_label, test_size=0.2, random_state=6)print("训练集的特征值：\n", x_train.shape)print("测试集的标签值：\n", y_test.shape)print("The length of original data X is:", data_features.shape[0])print("The length of train Data is:", x_train.shape[0])print("The length of test Data is:", x_test.shape[0])
训练集的特征值： (2310, 8) 测试集的标签值： (578,) The length of original data X is: 2888 The length of train Data is: 2310 The length of test Data is: 578
In [6]
x_train=x_train.reset_index(drop=True) x_test=x_test.reset_index(drop=True) y_train=y_train.reset_index(drop=True) y_test=y_test.reset_index(drop=True)
3.4.4 转换数组
对重置完成的各个数据集，将数据转换成矩阵形式供后续使用。
In [7]
x_train=np.array(x_train) x_test=np.array(x_test) y_train=np.array(y_train) y_test=np.array(y_test)
In [ ]
y_train
array([-0.987, 1.142, -2.555, ..., -0.42 , 1.024, 1.046])
In [8]
y_train = np.array(y_train) y_train = y_train.reshape(-1,1) y_test = np.array(y_test) y_test = y_test.reshape(-1,1)
3. 5 归一化（标准化）
- 对训练集与测试集进行归一化或者标准化处理
- 本处我们选择标准化
In [ ]
from sklearn.preprocessing import MinMaxScalerfrom sklearn.preprocessing import StandardScaler# 1. 实例化一个转换器类transfer = StandardScaler()# 2. 标准化x_train = transfer.fit_transform(x_train) x_test = transfer.fit_transform(x_test) y_train = transfer.fit_transform(y_train) y_test = transfer.fit_transform(y_test)# df_test_x = transfer.fit_transform(df_test)
In [ ]
x_train[0]
array([-0.187, -0.33 , -1.523, 0.6 , -0.437, 0.389, -0.626, -0.057])
3.6 设置随机数种子
In [ ]
import randomimport paddle
seed = 666# 设置随机种子 固定结果def set_seed(seed):
np.random.seed(seed)
random.seed(seed)
paddle.seed(seed)
set_seed(seed)
4. 模型构建
搭建全连接神经网络
- 8节点输入
- 1输出节点
In [9]
import paddleimport paddle.nn as nn# 定义动态图class Classification(paddle.nn.Layer):
def __init__(self):
super(Classification, self).__init__()
self.fc1 = paddle.nn.Linear(8, 1)
# 网络的前向计算函数
def forward(self, inputs):
pred = self.fc1(inputs) return pred
5. 可视化损失函数
In [11]
train_nums = []
train_costs = []def draw_train_process(iters,train_costs):
title="training cost"
plt.title(title, fontsize=24)
plt.xlabel("iter", fontsize=14)
plt.ylabel("cost", fontsize=14)
plt.plot(iters, train_costs,color='red',label='training cost')
plt.grid()
plt.show()
6. 自定义损失函数
In [20]
import paddleimport paddle.nn.functional as Fclass kl_loss(paddle.nn.Layer):
def __init__(self):
super(kl_loss, self).__init__() def forward(self, p, q, label):
ce_loss = 0.5 * (F.mse_loss(p, label=label)) + F.mse_loss(q, label=label)
kl_loss = self.compute_kl_loss(p, q) # carefully choose hyper-parameters
loss = ce_loss + 0.3 * kl_loss
return loss def compute_kl_loss(self, p, q):
p_loss = F.kl_div(F.log_softmax(p, axis=-1), F.softmax(q, axis=-1), reduction='none')
q_loss = F.kl_div(F.log_softmax(q, axis=-1), F.softmax(p, axis=-1), reduction='none') # You can choose whether to use function "sum" and "mean" depending on your task
p_loss = p_loss.sum()
q_loss = q_loss.sum()
loss = (p_loss + q_loss) / 2
return loss
7. 模型训练
7.1 开启训练
超参数设定如下：
- BATCH_SIZE = 16
- EPOCH_NUM = 20
- learning_rate=0.0002 损失函数：
- kl_loss
In [33]
import paddle.nn.functional as Fimport paddle
y_preds = []
labels_list = []
BATCH_SIZE = 16train_data = x_train
train_data_y = y_train
test_data = x_test
test_data_y = y_test
compute_kl_loss = kl_loss()
CET_loss = paddle.nn.CrossEntropyLoss()def train(model):
print('start training ... ') # 开启模型训练模式
model.train()
EPOCH_NUM = 20
train_num = 0
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=0.0002, T_max=int(train_data.shape[0]/BATCH_SIZE*EPOCH_NUM), verbose=False)
optimizer = paddle.optimizer.Adam(learning_rate=scheduler, parameters=model.parameters()) for epoch_id in range(EPOCH_NUM): # 在每轮迭代开始之前，将训练数据的顺序随机的打乱
np.random.shuffle(train_data) # 将训练数据进行拆分，每个batch包含8条数据
mini_batches = [np.append(train_data[k: k+BATCH_SIZE], train_data_y[k: k+BATCH_SIZE], axis = 1) for k in range(0, len(train_data), BATCH_SIZE)] for batch_id, data in enumerate(mini_batches):
features_np = np.array(data[:, :8], np.float32)
labels_np = np.array(data[:, -1:], np.float32)
features = paddle.to_tensor(features_np)
labels = paddle.to_tensor(labels_np) #前向计算
#y_pred = model(features)
y_pred1 = model(features)
y_pred2 = model(features)
cost = compute_kl_loss(y_pred1, y_pred2, label=labels) # cost = CET_loss(y_pred, labels)
#cost = F.mse_loss(y_pred, label=labels)
train_cost = cost.numpy()[0] #反向传播
cost.backward() #最小化loss，更新参数
optimizer.step() # 清除梯度
optimizer.clear_grad() if batch_id % 500 == 0 and epoch_id % 1 == 0: print("Pass:%d,Cost:%0.5f"%(epoch_id, train_cost))
train_num = train_num + BATCH_SIZE
train_nums.append(train_num)
train_costs.append(train_cost)
model = Classification()
train(model)
start training ... Pass:0,Cost:2.43391 Pass:1,Cost:3.92359 Pass:2,Cost:2.98257 Pass:3,Cost:2.93184 Pass:4,Cost:2.18770 Pass:5,Cost:3.19956 Pass:6,Cost:4.07202 Pass:7,Cost:2.55369 Pass:8,Cost:3.19636 Pass:9,Cost:3.43391 Pass:10,Cost:2.27505 Pass:11,Cost:1.95374 Pass:12,Cost:2.40070 Pass:13,Cost:3.80006 Pass:14,Cost:2.00660 Pass:15,Cost:3.59392 Pass:16,Cost:2.63512 Pass:17,Cost:2.65104 Pass:18,Cost:2.91626 Pass:19,Cost:2.96661
7.2 训练过程可视化
In [41]
import matplotlibimport matplotlib.pyplot as pltimport warnings
warnings.filterwarnings('ignore')
%matplotlib inline
draw_train_process(train_nums, train_costs)
<Figure size 640x480 with 1 Axes>
8. 模型预测
- 使用测试集数据进行预测
- 对预测后的数据进行形状转换方便后续可视化
In [36]
train_data = x_train
train_data_y = y_train
test_data = x_test
test_data_y = y_testdef predict(model):
print('start evaluating ... ')
model.eval()
outputs = []
mini_batches = [np.append(test_data[k: k+BATCH_SIZE], test_data_y[k: k+BATCH_SIZE], axis = 1) for k in range(0, len(test_data), BATCH_SIZE)] for data in mini_batches:
features_np = np.array(data[:, :8], np.float32)
features = paddle.to_tensor(features_np)
pred = model(features)
out = paddle.argmax(pred, axis=1)
outputs.extend(out.numpy()) return outputs
outputs = predict(model)
start evaluating ...
In [38]
predict_result = []for infer_feature in test_data:
infer_feature = infer_feature.reshape(1, 8)
infer_feature = paddle.to_tensor(np.array(infer_feature, dtype='float32'))
result = model(infer_feature)
predict_result.append(result)print(predict_result)
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[[0.41777393]])]
In [39]
predict_result = np.array(predict_result) predict_result_new = predict_result.reshape(-1,1) test_data_y_new = test_data_y
9. 可视化预测值与真实值
In [40]
# 绘制预测与真值结果plt.figure(figsize=(12,5), dpi=80)
plt.plot(test_data_y_new[:100], label="True value")
plt.plot(predict_result_new[:100], label="Pred value")
plt.xlabel("Sample",fontproperties = 'Times New Roman', size = 18)
plt.ylabel("Value",fontproperties = 'Times New Roman', size = 18)
plt.legend(loc='best')
plt.yticks(fontproperties = 'Times New Roman', size = 18)
plt.xticks(fontproperties = 'Times New Roman', size = 18)
plt.title("True VS Pred",fontproperties = 'Times New Roman', size = 18)
plt.legend(loc="best")
plt.show()
<Figure size 960x400 with 1 Axes>
10. 总结
- 本项目搭建人工神经网络实现了蒸汽量回归预测。
- 做了数据处理部分，包括异常值处理、相关性分析等。
- 在今后，可以考虑使用其他网络模型进行预测，例如卷积网络。
- 另外，可以尝试进一步优化超参数来优化模型。
