PyTorch搭建LSTM实现时间序列负荷预测_Python

I. 前言

在上一篇文章深入理解PyTorch中LSTM的输入和输出（从input输入到Linear输出）中，我详细地解释了如何利用PyTorch来搭建一个LSTM模型，本篇文章的主要目的是搭建一个LSTM模型用于时间序列预测。

系列文章：

PyTorch搭建LSTM实现多变量多步长时序负荷预测

PyTorch搭建LSTM实现多变量时序负荷预测

PyTorch深度学习LSTM从input输入到Linear输出

PyTorch搭建双向LSTM实现时间序列负荷预测

II. 数据处理

数据集为某个地区某段时间内的电力负荷数据，除了负荷以外，还包括温度、湿度等信息。

本篇文章暂时不考虑其它变量，只考虑用历史负荷来预测未来负荷。

本文中，我们根据前24个时刻的负荷下一时刻的负荷。有关多变量预测请参考：PyTorch搭建LSTM实现多变量时间序列预测（负荷预测）。

def load_data(file_name):
  global MAX, MIN
  df = pd.read_csv('data/new_data/' + file_name, encoding='gbk')
  columns = df.columns
  df.fillna(df.mean(), inplace=True)
  MAX = np.max(df[columns[1]])
  MIN = np.min(df[columns[1]])
  df[columns[1]] = (df[columns[1]] - MIN) / (MAX - MIN)
  return df
class MyDataset(Dataset):
  def __init__(self, data):
      self.data = data
  def __getitem__(self, item):
      return self.data[item]
  def __len__(self):
      return len(self.data)
def nn_seq(file_name, B):
  print('处理数据:')
  data = load_data(file_name)
  load = data[data.columns[1]]
  load = load.tolist()
  load = torch.FloatTensor(load).view(-1)
  data = data.values.tolist()
  seq = []
  for i in range(len(data) - 24):
      train_seq = []
      train_label = []
      for j in range(i, i + 24):
          train_seq.append(load[j])
      train_label.append(load[i + 24])
      train_seq = torch.FloatTensor(train_seq).view(-1)
      train_label = torch.FloatTensor(train_label).view(-1)
      seq.append((train_seq, train_label))
  # print(seq[:5])
  Dtr = seq[0:int(len(seq) * 0.7)]
  Dte = seq[int(len(seq) * 0.7):len(seq)]
  train_len = int(len(Dtr) / B) * B
  test_len = int(len(Dte) / B) * B
  Dtr, Dte = Dtr[:train_len], Dte[:test_len]
  train = MyDataset(Dtr)
  test = MyDataset(Dte)
  Dtr = DataLoader(dataset=train, batch_size=B, shuffle=False, num_workers=0)
  Dte = DataLoader(dataset=test, batch_size=B, shuffle=False, num_workers=0)
  return Dtr, Dte

上面代码用了DataLoader来对原始数据进行处理，最终得到了batch_size=B的数据集Dtr和Dte，Dtr为训练集，Dte为测试集。

III. LSTM模型

这里采用了深入理解PyTorch中LSTM的输入和输出（从input输入到Linear输出）中的模型：

class LSTM(nn.Module):
  def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size):
      super().__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.num_layers = num_layers
      self.output_size = output_size
      self.num_directions = 1 # 单向LSTM
      self.batch_size = batch_size
      self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
      self.linear = nn.Linear(self.hidden_size, self.output_size)
  def forward(self, input_seq):
      h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device)
      c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device)
      seq_len = input_seq.shape[1] # (5, 24)
      # input(batch_size, seq_len, input_size)
      input_seq = input_seq.view(self.batch_size, seq_len, 1)  # (5, 24, 1)
      # output(batch_size, seq_len, num_directions * hidden_size)
      output, _ = self.lstm(input_seq, (h_0, c_0)) # output(5, 24, 64)
      output = output.contiguous().view(self.batch_size * seq_len, self.hidden_size) # (5 * 24, 64)
      pred = self.linear(output) # pred(150, 1)
      pred = pred.view(self.batch_size, seq_len, -1) # (5, 24, 1)
      pred = pred[:, -1, :]  # (5, 1)
      return pred

IV. 训练

def LSTM_train(name, b):
  Dtr, Dte = nn_seq(file_name=name, B=b)
  input_size, hidden_size, num_layers, output_size = 1, 64, 5, 1
  model = LSTM(input_size, hidden_size, num_layers, output_size, batch_size=b).to(device)
  loss_function = nn.MSELoss().to(device)
  optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
  # 训练
  epochs = 15
  cnt = 0
  for i in range(epochs):
      cnt = 0
      print('当前', i)
      for (seq, label) in Dtr:
          cnt += 1
          seq = seq.to(device)
          label = label.to(device)
          y_pred = model(seq)
          loss = loss_function(y_pred, label)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          if cnt % 100 == 0:
              print('epoch', i, ':', cnt - 100, '~', cnt, loss.item())
  state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict()}
  torch.save(state, LSTM_PATH)

一共训练了15轮：

PyTorch搭建LSTM实现时间序列负荷预测

V. 测试

def test(name, b):
  global MAX, MIN
  Dtr, Dte = nn_seq(file_name=name, B=b)
  pred = []
  y = []
  print('loading model...')
  input_size, hidden_size, num_layers, output_size = 1, 64, 5, 1
  model = LSTM(input_size, hidden_size, num_layers, output_size, batch_size=b).to(device)
  model.load_state_dict(torch.load(LSTM_PATH)['model'])
  model.eval()
  print('predicting...')
  for (seq, target) in Dte:
      target = list(chain.from_iterable(target.data.tolist()))
      y.extend(target)
      seq = seq.to(device)
      seq_len = seq.shape[1]
      seq = seq.view(model.batch_size, seq_len, 1)  # (5, 24, 1)
      with torch.no_grad():
          y_pred = model(seq)
          y_pred = list(chain.from_iterable(y_pred.data.tolist()))
          pred.extend(y_pred)
  y, pred = np.array(y), np.array(pred)
  y = (MAX - MIN) * y + MIN
  pred = (MAX - MIN) * pred + MIN
  print('accuracy:', get_mape(y, pred))
  # plot
  x = [i for i in range(1, 151)]
  x_smooth = np.linspace(np.min(x), np.max(x), 600)
  y_smooth = make_interp_spline(x, y[0:150])(x_smooth)
  plt.plot(x_smooth, y_smooth, c='green', marker='*', ms=1, alpha=0.75, label='true')
  y_smooth = make_interp_spline(x, pred[0:150])(x_smooth)
  plt.plot(x_smooth, y_smooth, c='red', marker='o', ms=1, alpha=0.75, label='pred')
  plt.grid(axis='y')
  plt.legend()
  plt.show()

MAPE为6.07%：

PyTorch搭建LSTM实现时间序列负荷预测