滑县做网站公司,网页版梦幻西游红拂女,怎样给网站做超链接,北京产品设计公司有哪些1、工业场景
冶金过程中生产的各种煤气#xff0c;例如高炉煤气、焦炉煤气、转炉煤气等。作为重要的副产品和二次能源#xff0c;保证它们的梯级利用和减少放散是煤气能源平衡调控的一项紧迫任务#xff0c;准确的预测煤气的发生量是实现煤气系统在线最优调控的前提。
2、…1、工业场景
冶金过程中生产的各种煤气例如高炉煤气、焦炉煤气、转炉煤气等。作为重要的副产品和二次能源保证它们的梯级利用和减少放散是煤气能源平衡调控的一项紧迫任务准确的预测煤气的发生量是实现煤气系统在线最优调控的前提。
2、数学模型
本次研究主要采用了长短记忆模型LSTM预测了正常工况下的高炉煤气发生量。后续研究方向希望将正常工况扩展到变化工况条件下例如休风、减产、停产、检修等条件下煤气发生量并引入更多特征维度例如焦比、煤比、风量、富氧、风温、风压、炉内压差等。下面重点介绍一下LSTM网络结构。
长短期记忆网络LSTMLong Short-Term Memory是一种时间循环神经网络是为了解决一般的RNN循环神经网络存在的长期依赖问题而专门设计出来的所有的RNN都具有一种重复神经网络模块的链式形式。 这张图片是经典的LSTM网络结构的图片但是不好理解图片把多维的空间结构压缩成了二维图片所以需要大家脑补一下回放到立体空间中去理解。相对比RNN 通过时间步骤地更新隐藏状态和输出结果。而LSTM 通过输入门、遗忘门和输出门来控制隐藏状态的更新和输出。在这里不过多的去讲解模型结构大家可以从网上了解一下LSTM网络模型作为世纪霸主被广泛应用于自然语言处理、语音识别、图像处理等领域。总之很厉害
3、数据准备
本次数据收集了1#高炉从4月12日0点到4月16日0点72小时的煤气累计发生量数据时间间隔为60000ms也就是1分钟一次点位数据通过计算获取5760个分钟级的煤气发生量数据。 如图第一个字段是数采时序库中的点位标识第二个字段是时间第三个字段是当前煤气发生量累计值第四个字段是计算获得的当前分钟内煤气发生量。 4、模型构建
class LSTM(nn.Module):LSMT网络搭建def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size):super().__init__()self.input_size input_sizeself.hidden_size hidden_sizeself.num_layers num_layersself.output_size output_sizeself.num_directions 1 # 单向LSTMself.batch_size batch_sizeself.lstm nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_firstTrue)self.linear nn.Linear(self.hidden_size, self.output_size)def forward(self, input_seq):batch_size, seq_len input_seq.shape[0], input_seq.shape[1]h_0 torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size)c_0 torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size)output, _ self.lstm(input_seq, (h_0, c_0))pred self.linear(output)pred pred[:, -1, :]return pred
5、模型训练
①数据预处理加载数据文件原始数据文件为csv通过1#高炉84小时内煤气发生量累计值时间间隔为60000ms计算出84小时内每分钟的发生量将数据分为训练集:验证集:测试集3:1:1。
def load_data(file_name):加载数据文件原始数据文件为csv通过1#高炉24小时内煤气发生量累计值时间间隔为60000ms计算出24小时内每分钟的发生量:param file_name csv文件的绝对路径:return 训练集、验证集、测试集、训练集中最大值、训练集中最小值训练集:验证集:测试集3:1:1dataset pd.read_csv(D:\\LIHAOWORK\\ file_name, encodinggbk)train dataset[:int(len(dataset) * 0.6)]val dataset[int(len(dataset) * 0.6):int(len(dataset) * 0.8)]test dataset[int(len(dataset) * 0.8):len(dataset)]max, nin np.max(train[train.columns[3]]), np.min(train[train.columns[3]]) # 分钟内发生量是csv中第四个字段return train, val, test, max, nin
②数据组装把数据组装成训练、验证、测试需要的格式。实质是90个顺序数据为一组作为输入值x第91个作为真实发生值y如此循环。
def process_data(data, batch_size, shuffle, m, n, k):数据处理:param data 待处理数据:param batch_size 批量大小:param shuffle 是否打乱:param m 最大值:param n 最小值:param k 序列长度:return 处理好的数据data_3 data.iloc[0:, [3]].to_numpy().reshape(-1) #data_3 data_3.tolist()data data.values.tolist()data_3 (data_3 - n) / (m - n)feature []label []for i in range(len(data) - k):train_seq []train_label []for j in range(i, i k):x [data_3[j]]train_seq.append(x)train_label.append(data_3[i k])feature.append(train_seq)label.append(train_label)feature_tensor torch.FloatTensor(feature)label_tensor torch.FloatTensor(label)data TensorDataset(feature_tensor, label_tensor)data_loader DataLoader(datasetdata, batch_sizebatch_size, shuffleshuffle, num_workers0, drop_lastTrue)return data_loader
③模型训练输入大小为1隐藏层大小为128隐藏层数为1输出大小为1批量大小为5学习率为0.01训练次数3。
def train(Dtr, Val, path):训练:param Dtr 训练集:param Val 验证集:param path 模型保持路劲input_size 1hidden_size 128num_layers 1output_size 1epochs 5model LSTM(input_size, hidden_size, num_layers, output_size, batch_size5)loss_function nn.MSELoss()optimizer torch.optim.Adam(model.parameters(), lr0.01, weight_decay1e-4)# optimizer torch.optim.SGD(model.parameters(), lr0.05, momentum0.9, weight_decay1e-4)scheduler StepLR(optimizer, step_size30, gamma0.1)# start trainingfor epoch in tqdm(range(epochs)):train_loss []for (seq, label) in Dtr:y_pred model(seq)loss loss_function(y_pred, label)train_loss.append(loss.item())optimizer.zero_grad()loss.backward()optimizer.step()scheduler.step()# validationmodel.eval()val_loss []for seq, label in Val:y_pred model(seq)loss loss_function(y_pred, label)val_loss.append(loss.item())print(epoch {:03d} train_loss {:.8f} val_loss {:.8f}.format(epoch, np.mean(train_loss), np.mean(val_loss)))model.train()state {models: model.state_dict()}torch.save(state, path)
训练过程如下
④模型测试
def test(Dte, path, m, n):测试:param Dte 测试集:param path 模型:param m 最大值:param n 最小值pred []y []input_size 1hidden_size 128num_layers 1output_size 1model LSTM(input_size, hidden_size, num_layers, output_size, batch_size5)model.load_state_dict(torch.load(path)[models])model.eval()for (seq, target) in tqdm(Dte):y.extend(target)with torch.no_grad():y_pred model(seq)pred.extend(y_pred)y, pred np.array(y), np.array(pred)y (m - n) * y npred (m - n) * pred n# 出图x [i for i in range(len(y))]x_smooth np.linspace(np.min(x), np.max(x), 1000)y_smooth make_interp_spline(x, y)(x_smooth)plt.plot(x_smooth, y_smooth, cgreen, marker*, ms1, alpha1, labeltrue)y_smooth make_interp_spline(x, pred)(x_smooth)plt.plot(x_smooth, y_smooth, cred, markero, ms1, alpha1, labelpred)plt.grid(axisy)plt.legend()plt.show()直接对比图红色线为模型在测试数据集上的预测值绿色线为真实的发生值。 有点密密麻麻我们可以看一下局部 ⑤滚动测试30分钟内发生值利用前90分钟的真实发生值预测第91分钟的发生值再将第91分钟的发生值加入到输入中预测第92分钟的发生值以此类推预测30分钟内的高炉煤气发生量。
def test_rolling(Dte, path, m, n):滚动测试预测30条:param Dte 测试集:param path 模型:param m 最大值:param n 最小值pred []y []input_size 1hidden_size 128num_layers 1output_size 1model LSTM(input_size, hidden_size, num_layers, output_size, batch_size1)model.load_state_dict(torch.load(path)[models])model.eval()i 0 # 控制滚动预测的长度这里计划通过前90分钟内的发生量预测后30分钟内的发生量for (seq, target) in tqdm(Dte):y.extend(target)with torch.no_grad():seq seq.numpy().tolist()[0]seq.extend(pred) # 预测值追加到后面seq seq[-90:] # 截取后90条数据滚动预测seq torch.tensor(seq).resize(1, 90, 1)y_pred model(seq)pred.extend(y_pred)i i 1if i 30: # 控制滚动预测的长度这里计划通过前90分钟内的发生量预测后30分钟内的发生量breaky, pred np.array(y), np.array(pred)y (m - n) * y npred (m - n) * pred n# 出图x [i for i in range(len(y))]x_smooth np.linspace(np.min(x), np.max(x), 1000)y_smooth make_interp_spline(x, y)(x_smooth)plt.plot(x_smooth, y_smooth, cgreen, marker*, ms1, alpha1, labeltrue)y_smooth make_interp_spline(x, pred)(x_smooth)plt.plot(x_smooth, y_smooth, cred, markero, ms1, alpha1, labelpred)plt.grid(axisy)plt.legend()plt.show()
出图 有点过分拟合 调整参数num_layers 2 增加dropout0.3 后 整体效果不是很理想可能再调一下参会好一些。 目前的完整代码如下
import pandas as pd
import numpy as np
import torch
from matplotlib import pyplot as plt
from scipy.interpolate import make_interp_spline
from torch import nn
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdmdef load_data(file_name):加载数据文件原始数据文件为csv通过1#高炉24小时内煤气发生量累计值时间间隔为60000ms计算出24小时内每分钟的发生量:param file_name csv文件的绝对路径:return 训练集、验证集、测试集、训练集中最大值、训练集中最小值训练集:验证集:测试集3:1:1dataset pd.read_csv(D:\\LIHAOWORK\\ file_name, encodinggbk)train dataset[:int(len(dataset) * 0.6)]val dataset[int(len(dataset) * 0.6):int(len(dataset) * 0.8)]test dataset[int(len(dataset) * 0.8):len(dataset)]max, nin np.max(train[train.columns[3]]), np.min(train[train.columns[3]]) # 分钟内发生量是csv中第四个字段return train, val, test, max, nindef process_data(data, batch_size, shuffle, m, n, k):数据处理:param data 待处理数据:param batch_size 批量大小:param shuffle 是否打乱:param m 最大值:param n 最小值:param k 序列长度:return 处理好的数据data_3 data.iloc[0:, [3]].to_numpy().reshape(-1) #data_3 data_3.tolist()data data.values.tolist()data_3 (data_3 - n) / (m - n)feature []label []for i in range(len(data) - k):train_seq []train_label []for j in range(i, i k):x [data_3[j]]train_seq.append(x)train_label.append(data_3[i k])feature.append(train_seq)label.append(train_label)feature_tensor torch.FloatTensor(feature)label_tensor torch.FloatTensor(label)data TensorDataset(feature_tensor, label_tensor)data_loader DataLoader(datasetdata, batch_sizebatch_size, shuffleshuffle, num_workers0, drop_lastTrue)return data_loaderclass LSTM(nn.Module):LSMT网络搭建def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size):super().__init__()self.input_size input_sizeself.hidden_size hidden_sizeself.num_layers num_layersself.output_size output_sizeself.num_directions 1 # 单向LSTMself.batch_size batch_sizeself.lstm nn.LSTM(self.input_size, self.hidden_size, self.num_layers, dropout0.3, batch_firstTrue)self.linear nn.Linear(self.hidden_size, self.output_size)def forward(self, input_seq):batch_size, seq_len input_seq.shape[0], input_seq.shape[1]h_0 torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size)c_0 torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size)output, _ self.lstm(input_seq, (h_0, c_0))pred self.linear(output)pred pred[:, -1, :]return preddef train(Dtr, Val, path):训练:param Dtr 训练集:param Val 验证集:param path 模型保持路劲input_size 1hidden_size 128num_layers 2output_size 1epochs 3model LSTM(input_size, hidden_size, num_layers, output_size, batch_size5)loss_function nn.MSELoss()optimizer torch.optim.Adam(model.parameters(), lr0.01, weight_decay1e-4)# optimizer torch.optim.SGD(model.parameters(), lr0.05, momentum0.9, weight_decay1e-4)scheduler StepLR(optimizer, step_size30, gamma0.1)# start trainingfor epoch in tqdm(range(epochs)):train_loss []for (seq, label) in Dtr:y_pred model(seq)loss loss_function(y_pred, label)train_loss.append(loss.item())optimizer.zero_grad()loss.backward()optimizer.step()scheduler.step()# validationmodel.eval()val_loss []for seq, label in Val:y_pred model(seq)loss loss_function(y_pred, label)val_loss.append(loss.item())print(epoch {:03d} train_loss {:.8f} val_loss {:.8f}.format(epoch, np.mean(train_loss), np.mean(val_loss)))model.train()state {models: model.state_dict()}torch.save(state, path)def test(Dte, path, m, n):测试:param Dte 测试集:param path 模型:param m 最大值:param n 最小值pred []y []input_size 1hidden_size 128num_layers 2output_size 1model LSTM(input_size, hidden_size, num_layers, output_size, batch_size5)model.load_state_dict(torch.load(path)[models])model.eval()for (seq, target) in tqdm(Dte):y.extend(target)with torch.no_grad():y_pred model(seq)pred.extend(y_pred)y, pred np.array(y), np.array(pred)y (m - n) * y npred (m - n) * pred n# 出图x [i for i in range(len(y))]x_smooth np.linspace(np.min(x), np.max(x), 1000)y_smooth make_interp_spline(x, y)(x_smooth)plt.plot(x_smooth, y_smooth, cgreen, marker*, ms1, alpha1, labeltrue)y_smooth make_interp_spline(x, pred)(x_smooth)plt.plot(x_smooth, y_smooth, cred, markero, ms1, alpha1, labelpred)plt.grid(axisy)plt.legend()plt.show()def test_rolling(Dte, path, m, n):滚动测试预测30条:param Dte 测试集:param path 模型:param m 最大值:param n 最小值pred []y []input_size 1hidden_size 128num_layers 2output_size 1model LSTM(input_size, hidden_size, num_layers, output_size, batch_size1)model.load_state_dict(torch.load(path)[models])model.eval()i 0 # 控制滚动预测的长度这里计划通过前90分钟内的发生量预测后30分钟内的发生量for (seq, target) in tqdm(Dte):y.extend(target)with torch.no_grad():seq seq.numpy().tolist()[0]seq.extend(pred) # 预测值追加到后面seq seq[-90:] # 截取后90条数据滚动预测seq torch.tensor(seq).resize(1, 90, 1)y_pred model(seq)pred.extend(y_pred)i i 1if i 30: # 控制滚动预测的长度这里计划通过前90分钟内的发生量预测后30分钟内的发生量breaky, pred np.array(y), np.array(pred)y (m - n) * y npred (m - n) * pred n# 出图x [i for i in range(len(y))]x_smooth np.linspace(np.min(x), np.max(x), 1000)y_smooth make_interp_spline(x, y)(x_smooth)plt.plot(x_smooth, y_smooth, cgreen, marker*, ms1, alpha1, labeltrue)y_smooth make_interp_spline(x, pred)(x_smooth)plt.plot(x_smooth, y_smooth, cred, markero, ms1, alpha1, labelpred)plt.grid(axisy)plt.legend()plt.show()if __name__ __main__:Dtr, Val, Dte, m, n load_data(4.csv)Dtr process_data(Dtr, 5, False, m, n, 90)Val process_data(Val, 5, False, m, n, 90)#Dte process_data(Dte, 5, False, m, n, 90)Dte process_data(Dte, 1, False, m, n, 90)train(Dtr, Val, D:\\LIHAOWORK\\model.pth)#test(Dte, D:\\LIHAOWORK\\model.pth, m, n)test_rolling(Dte, D:\\LIHAOWORK\\model.pth, m, n)
