本案例适合理工科。

承接上一篇的硬核案例：Python数据分析案例24——基于深度学习的锂电池寿命预测

本次案例类似，只是进一步拓展了时间序列预测到多变量的情况。上一个案例的时间序列都是只有电池容量一个特征变量，现在采用多个变量进行神经网络模型的构建。

案例背景

海上风电是最佳很热门的工程，准确预测自然很重要。

本次简单使用一些常见的神经网络进行预测效果对比。(试试手的小案例)

数据集有很多特征，如下：

V是风速，D是风向，还有什么空气湿度balabala一堆特征，最后的一列是电功率。

代码准备

和上一篇案例差不多，都是有大量的自定义函数。

首先导入包，

import os
import math
import time
import datetime
import random as rn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams ['font.sans-serif'] ='SimHei'               #显示中文
plt.rcParams ['axes.unicode_minus']=False               #显示负号from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error,r2_scoreimport tensorflow as tf
import keras
from keras.models import Model, Sequential
from keras.layers import GRU, Dense,Conv1D, MaxPooling1D,GlobalMaxPooling1D,Embedding,Dropout,Flatten,SimpleRNN,LSTM
from keras.callbacks import EarlyStopping
#from tensorflow.keras import regularizers
#from keras.utils.np_utils import to_categorical
from tensorflow.keras  import optimizers

读取数据，由于数据量太大，我就只取了前1000条来试试水

data0=pd.read_excel('5.xlsx').iloc[:1000,:].set_index('Sequence No.').rename(columns={'y (% relative to rated power)':'y'})
data0.head()

 定义随机数种子函数和评估函数

def set_my_seed():os.environ['PYTHONHASHSEED'] = '0'np.random.seed(1)rn.seed(12345)tf.random.set_seed(123)def evaluation(y_test, y_predict):mae = mean_absolute_error(y_test, y_predict)mse = mean_squared_error(y_test, y_predict)rmse = np.sqrt(mean_squared_error(y_test, y_predict))mape=(abs(y_predict -y_test)/ y_test).mean()r_2=r2_score(y_test, y_predict)return mae, rmse, mape,r_2  #mse

构建序列数据的测试集和训练集函数

def build_sequences(text, window_size=24):#text:list of capacityx, y = [],[]for i in range(len(text) - window_size):sequence = text[i:i+window_size]target = text[i+window_size]x.append(sequence)y.append(target)return np.array(x), np.array(y)def get_traintest(data,train_size=len(data0),window_size=24):train=data[:train_size]test=data[train_size-window_size:]X_train,y_train=build_sequences(train,window_size=window_size)X_test,y_test=build_sequences(test,window_size=window_size)return X_train,y_train[:,-1],X_test,y_test[:,-1]

构建五种模型函数，还有画损失图和画拟合图的函数。

def build_model(X_train,mode='LSTM',hidden_dim=[32,16]):set_my_seed()model = Sequential()if mode=='RNN':#RNNmodel.add(SimpleRNN(hidden_dim[0],return_sequences=True, input_shape=(X_train.shape[-2],X_train.shape[-1])))model.add(SimpleRNN(hidden_dim[1]))     elif mode=='MLP':model.add(Dense(hidden_dim[0],activation='relu',input_shape=(X_train.shape[-2],X_train.shape[-1])))model.add(Flatten())model.add(Dense(hidden_dim[1],activation='relu'))elif mode=='LSTM':# LSTMmodel.add(LSTM(hidden_dim[0],return_sequences=True, input_shape=(X_train.shape[-2],X_train.shape[-1])))model.add(LSTM(hidden_dim[1]))elif mode=='GRU':#GRUmodel.add(GRU(hidden_dim[0],return_sequences=True, input_shape=(X_train.shape[-2],X_train.shape[-1])))model.add(GRU(hidden_dim[1]))elif mode=='CNN':#一维卷积model.add(Conv1D(hidden_dim[0], kernel_size=3, padding='causal', strides=1, activation='relu', dilation_rate=1, input_shape=(X_train.shape[-2],X_train.shape[-1])))#model.add(MaxPooling1D())model.add(Conv1D(hidden_dim[1], kernel_size=3, padding='causal', strides=1, activation='relu', dilation_rate=2))#model.add(MaxPooling1D())model.add(Flatten())model.add(Dense(1))model.compile(optimizer='Adam', loss='mse',metrics=[tf.keras.metrics.RootMeanSquaredError(),"mape","mae"])return modeldef plot_loss(hist,imfname=''):plt.subplots(1,4,figsize=(16,2))for i,key in enumerate(hist.history.keys()):n=int(str('14')+str(i+1))plt.subplot(n)plt.plot(hist.history[key], 'k', label=f'Training {key}')plt.title(f'{imfname} Training {key}')plt.xlabel('Epochs')plt.ylabel(key)plt.legend()plt.tight_layout()plt.show()
def plot_fit(y_test, y_pred):plt.figure(figsize=(4,2))plt.plot(y_test, color="red", label="actual")plt.plot(y_pred, color="blue", label="predict")plt.title(f"拟合值和真实值对比")plt.xlabel("Time")plt.ylabel('power')plt.legend()plt.show()

 定义训练函数，准备两个数据框，一个装评价指标，一个装预测结果。

df_eval_all=pd.DataFrame(columns=['MAE','RMSE','MAPE','R2'])
df_preds_all=pd.DataFrame()
def train_fuc(mode='LSTM',window_size=64,batch_size=32,epochs=50,hidden_dim=[32,16],train_ratio=0.8,show_loss=True,show_fit=True):#准备数据data=data0.to_numpy()#归一化scaler = MinMaxScaler() scaler = scaler.fit(data[:,:-1])X=scaler.transform(data[:,:-1])   y_scaler = MinMaxScaler() y_scaler = y_scaler.fit(data[:,-1].reshape(-1,1))y=y_scaler.transform(data[:,-1].reshape(-1,1))train_size=int(len(data)*train_ratio)X_train,y_train,X_test,y_test=get_traintest(np.c_[X,y],window_size=window_size,train_size=train_size)print(X_train.shape,y_train.shape,X_test.shape,y_test.shape)#构建模型s = time.time()set_my_seed()model=build_model(X_train=X_train,mode=mode,hidden_dim=hidden_dim)earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=5)hist=model.fit(X_train, y_train,batch_size=batch_size,epochs=epochs,callbacks=[earlystop],verbose=0)if show_loss:plot_loss(hist)#预测y_pred = model.predict(X_test)y_pred = y_scaler.inverse_transform(y_pred)y_test = y_scaler.inverse_transform(y_test.reshape(-1,1))#print(f'真实y的形状：{y_test.shape},预测y的形状：{y_pred.shape}')if show_fit:plot_fit(y_test, y_pred)e=time.time()print(f"运行时间为{round(e-s,3)}")df_preds_all[mode]=y_pred.reshape(-1,)s=list(evaluation(y_test, y_pred))df_eval_all.loc[f'{mode}',:]=ss=[round(i,3) for i in s]print(f'{mode}的预测效果为：MAE:{s[0]},RMSE:{s[1]},MAPE:{s[2]},R2:{s[3]}')print("=======================================运行结束==========================================")

初始化参数：

window_size=64
batch_size=32
epochs=50
hidden_dim=[32,16]
train_ratio=0.8
show_fit=True
show_loss=True
mode='LSTM'  #RNN,GRU,CNN

神经网络

上面封装了那么多自定义函数就是为了下面训练的方便，

传入参数就可以训练然后评价了。

LSTM网络

train_fuc(mode='LSTM')

 可以看到清楚地打印的损失图和拟合图，然后是误差指标的计算。

若想改参数，直接在训练函数里面改就行，例如想将滑动窗口改小一点，改为16，神经元个数也改多一点，改为128和32，这样写就行：

train_fuc(mode='LSTM',window_size=16,hidden_dim=[128,32])

可以看到，拟合优度上升了一点。

可以继续调整 。

 RNN预测

修改mode这个参数就行。

mode='RNN' 
set_my_seed()
train_fuc(mode=mode,window_size=window_size,batch_size=32,epochs=epochs,hidden_dim=hidden_dim)

GRU预测

mode='GRU' 
set_my_seed()
train_fuc(mode=mode,window_size=window_size,batch_size=batch_size,epochs=epochs,hidden_dim=hidden_dim)

效果很不错！ 

一维CNN

mode='CNN' 
set_my_seed()
train_fuc(mode=mode,window_size=window_size,batch_size=batch_size,epochs=epochs,hidden_dim=hidden_dim)

MLP

mode='MLP' 
set_my_seed()
train_fuc(mode=mode,window_size=window_size,batch_size=batch_size,epochs=60,hidden_dim=hidden_dim)

评价指标

查看：

df_eval_all

 可以看到模型的预测效果都还不错，都有90%以上，GRU等循环神经网络效果很好，LSTM调了一下参数，效果是最好的。

预测结果：

df_preds_all

 然后可以用这个画图什么的：