ICode9
精准搜索请尝试： 精确搜索
首页
编程语言>
数据库
系统相关
互联网
其他分享
Python
Java
JavaScript
android
PHP
首页 > 其他分享> 文章详细

太阳能装置效率预测

2021-10-17 16:59:03  阅读：222  来源： 互联网

标签：装置 rmse pred train import test 太阳能 model 效率


   我们将只保留一个站点，使用 scikit-learn 的基本 ML 模型进行一个月的预测，使用深度学习和tensorflow预测一到两天。
   性能指标：均方根误差，探索性分析可见，数据集是干净的：没有异常值，没有重复行，也没有缺失值。

1、基线模型

基线模型得到的结果，将会是其他模型结果的比较基准。

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from dataprepare import dataset_con
from visualize import plot_scores,plot_predictions
import warnings
warnings.filterwarnings("ignore")
pd.options.display.max_columns = 300

##原始数据
df = pd.read_csv("dataset\solar_generation_by_station.csv")
train_data,test_data = dataset_con(df)

model_instances, model_names, rmse_train, rmse_test = [], [], [], []

#构造训练集和测试集
x_train, y_train = train_data.drop(columns=['time']), train_data['FR10']
x_test, y_test = test_data.drop(columns=['time']), test_data['FR10']

# 基线模型，作为基准模型
def mean_df(d, h):
    "return the hourly mean of a specific day of the year"
    res = x_train[(x_train['day'] == d) & (x_train['hour'] == h)]['FR10'].mean()
    return res
#预测值添加到数据集
x_train['pred'] = x_train.apply(lambda x: mean_df(x.day, x.hour), axis=1)
x_test['pred'] = x_test.apply(lambda x: mean_df(x.day, x.hour), axis=1)
model_names.append("base_line")
rmse_train.append(np.sqrt(mean_squared_error(x_train['FR10'], x_train['FR10']))) # a modifier en pred
rmse_test.append(np.sqrt(mean_squared_error(x_test['FR10'], x_test['pred'])))
#显示上个月的预测（橙色）和实际值（蓝色）
plot_predictions(data=x_test[['FR10', 'pred']])

在这里插入图片描述

2、回归模型

下面会利用几种回归模型进行预测。将通过比较测试集上的性能，来判断哪个模型最有效。

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from dataprepare import dataset_con
from visualize import plot_scores,plot_predictions
import warnings
warnings.filterwarnings("ignore")
pd.options.display.max_columns = 300
##原始数据
df = pd.read_csv("F:\mygithub\Big_Data_Renewable_energies-master\dataset\solar_generation_by_station.csv")
train_data,test_data = dataset_con(df)
model_instances, model_names, rmse_train, rmse_test = [], [], [], []
#构造训练集和测试集
X_train, y_train = train_data[['month', 'week', 'day', 'hour']], train_data['FR10']
X_test, y_test = test_data[['month', 'week', 'day', 'hour']], test_data['FR10']
#训练的模型
from sklearn.neighbors import KNeighborsRegressor#k近邻
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet#线性回归，岭回归，Lasso回归，弹性网络
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.svm import LinearSVR
from sklearn.svm import SVR
import xgboost as xgb
#打印分数
def get_rmse(reg, model_name):
    """打印传入参数的模型的分数以及并返回训练/测试集上的分数"""
    y_train_pred, y_pred = reg.predict(X_train), reg.predict(X_test)
    rmse_train, rmse_test = np.sqrt(mean_squared_error(y_train, y_train_pred)), np.sqrt(
        mean_squared_error(y_test, y_pred))
    print(model_name, '\t - RMSE on Training  = {rmse_train:%f}'%rmse_train+' / RMSE on Test = {rmse_test:}'%rmse_test)

    return rmse_train, rmse_test
# 最初使用的所有基本模型的列表
model_list = [
    LinearRegression(), Lasso(), Ridge(), ElasticNet(),
    RandomForestRegressor(), GradientBoostingRegressor(), ExtraTreesRegressor(),
    xgb.XGBRegressor(), KNeighborsRegressor()
             ]
# 训练和测试的分数和名字列表创建
model_names.extend([str(m)[:str(m).index('(')] for m in model_list])
# 训练和测试所有模型
for model, name in zip(model_list, model_names):
    model.fit(X_train, y_train)
    sc_train, sc_test = get_rmse(model, name)
    rmse_train.append(sc_train)
    rmse_test.append(sc_test)

结果比较

base_line 	 - RMSE on Training  = 0.21 / RMSE on Test = 0.15
LinearRegression 	 - RMSE on Training  = 0.21 / RMSE on Test = 0.15
Lasso 	 - RMSE on Training  = 0.21 / RMSE on Test = 0.15
Ridge 	 - RMSE on Training  = 0.21 / RMSE on Test = 0.15
ElasticNet 	 - RMSE on Training  = 0.10 / RMSE on Test = 0.10
RandomForestRegressor 	 - RMSE on Training  = 0.11 / RMSE on Test = 0.09
GradientBoostingRegressor 	 - RMSE on Training  = 0.10 / RMSE on Test = 0.10
ExtraTreesRegressor 	 - RMSE on Training  = 0.11 / RMSE on Test = 0.09
XGBRegressor 	 - RMSE on Training  = 0.10 / RMSE on Test = 0.10
LGBMRegressor 	 - RMSE on Training  = 0.10 / RMSE on Test = 0.10

3、深度学习

尝试根据过去 2 天（48 小时）的所有特征（所有其他站效率）预测一小时的 FR10 值。

3.1 数据集构建

df = pd.read_csv("dataset\solar_generation_by_station.csv")
df = df[sorted([c for c in df.columns if 'FR' in c])]
# 只保留最近4年的FR数据
df = df[-24*365*4:]
# 数据处理函数：输入为df和lookback，输出的X的各个元素为4年来每个48小时的数据
def process_data(data, past):
    X = []
    for i in range(len(data)-past-1):
        X.append(data.iloc[i:i+past].values)
    return np.array(X)
#根据过去2天的特征值预测之后1个小时的值
lookback = 48
#仅针对FR10这个站点进行预测，y为FR10第一个48小时后的所有数据值，X的元素为y对应的数据值之前的48小时数据
y = df['FR10'][lookback+1:]
X = process_data(df, lookback)
from sklearn.model_selection import train_test_split
#划分训练集和测试集，不打乱
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle=False)

RNN,LSTM,GRU模型构建、训练与测试

'''
RNN
'''
from keras.models import Sequential
from keras.layers import SimpleRNN, Dense, Embedding, Dropout
# from tensorflow.keras.models import Sequential
# from tensorflow.keras.layers import SimpleRNN, Dense, Embedding, Dropout


def my_RNN():
    my_rnn = Sequential()
    my_rnn.add(SimpleRNN(units=32, return_sequences=True, input_shape=(lookback,22)))
    my_rnn.add(SimpleRNN(units=32, return_sequences=True))
    my_rnn.add(SimpleRNN(units=32, return_sequences=False))
    my_rnn.add(Dense(units=1, activation='linear'))
    return my_rnn


rnn_model = my_RNN()
rnn_model.compile(optimizer='adam', loss='mean_squared_error')
rnn_model.fit(x=X_train, y=y_train, validation_data=(X_test, y_test), epochs=50, batch_size=64)

y_pred_train, y_pred_test = rnn_model.predict(X_train), rnn_model.predict(X_test)
err_train_rnn, err_test_rnn = np.sqrt(mean_squared_error(y_train, y_pred_train)), np.sqrt(mean_squared_error(y_test, y_pred_test))

def append_results(model_name,err_train,err_test):
    model_names.append(model_name)
    rmse_train.append(err_train)
    rmse_test.append(err_test)

append_results("RNN",err_train_rnn,err_test_rnn)


plot_evolution(X_train,y_train,X_test,y_test,y_pred_test)
rnn_res = pd.DataFrame(zip(list(y_test), list(np.squeeze(y_pred_test))), columns =['FR10', 'pred'])
plot_predictions(data=rnn_res[-30*24:])

'''
GRU
'''

from keras.layers import GRU

def my_GRU(input_shape):
    my_GRU = Sequential()
    my_GRU.add(GRU(units=32, return_sequences=True, activation='relu', input_shape=input_shape))
    my_GRU.add(GRU(units=32, activation='relu', return_sequences=False))
    my_GRU.add(Dense(units=1, activation='linear'))
    return my_GRU

gru_model = my_GRU(X.shape[1:])
gru_model.compile(optimizer='adam', loss='mean_squared_error')
gru_model.fit(x=X_train, y=y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32)

y_pred_train, y_pred_test = gru_model.predict(X_train), gru_model.predict(X_test)
err_train_gru, err_test_gru = np.sqrt(mean_squared_error(y_train, y_pred_train)), np.sqrt(mean_squared_error(y_test, y_pred_test))

append_results("GRU",err_train_gru,err_test_gru)
plot_evolution(X_train,y_train,X_test,y_test,y_pred_test)

gru_res = pd.DataFrame(zip(list(y_test), list(np.squeeze(y_pred_test))), columns =['FR10', 'pred'])
plot_predictions(data=gru_res[-30*24:])

'''
LSTM
'''

from keras.layers import LSTM

def my_LSTM(input_shape):
    my_LSTM = Sequential()
    my_LSTM.add(LSTM(units=32, return_sequences=True, activation='relu', input_shape=input_shape))
    my_LSTM.add(LSTM(units=32, activation='relu', return_sequences=False))
    my_LSTM.add(Dense(units=1, activation='linear'))
    return my_LSTM

lstm_model = my_LSTM(X.shape[1:])
lstm_model.compile(optimizer='adam', loss='mean_squared_error')
lstm_model.fit(x=X_train, y=y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32)

y_pred_train, y_pred_test = lstm_model.predict(X_train), lstm_model.predict(X_test)
err_train_lstm, err_test_lstm = np.sqrt(mean_squared_error(y_train, y_pred_train)), np.sqrt(mean_squared_error(y_test, y_pred_test))
append_results("LSTM",err_train_lstm,err_test_lstm)
plot_evolution(X_train,y_train,X_test,y_test,y_pred_test)

lstm_res = pd.DataFrame(zip(list(y_test), list(np.squeeze(y_pred_test))), columns =['FR10', 'pred'])
plot_predictions(data=lstm_res[-30*24:])

plt.style.use('fivethirtyeight')
plot_scores(model_names,rmse_train,rmse_test)


df_score = pd.DataFrame({'model_names' : model_names, 'rmse_test' : rmse_test})

plt.figure(figsize=(12, 8))
sns.barplot(y="model_names", x="rmse_test", data=df_score, palette="Blues_d")
plt.title("Comparaison des erreurs pour chaque modèle", fontsize=20)
plt.xlabel('erreur RMSE - plus elle est petite, meilleur est le modèle', fontsize=16)
plt.ylabel('liste des modèles esssayés', fontsize=16)
plt.show()

所有模型结果
在这里插入图片描述

标签：装置,rmse,pred,train,import,test,太阳能,model,效率
来源： https://blog.csdn.net/qq_38384924/article/details/120812569

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。

关于我们 | 联系我们 | 留言反馈

专注分享技术，共同学习，共同进步。侵权联系[81616952@qq.com]

Copyright (C)ICode9.com, All Rights Reserved.

ICode9版权所有