├── LSTM-pytorch.zip
├── LSTM-pytorch
    ├── best_model.pt
    ├── __pycache__
    │   └── dataset_model.cpython-38.pyc
    ├── process_data
    │   ├── dataset_split.py
    │   └── 1.data_preparation.py
    ├── dataset_model.py
    ├── test.py
    ├── train.py
    └── dataset
    │   └── test_dataset.csv
├── README.md
└── .gitignore


/LSTM-pytorch.zip:
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https://raw.githubusercontent.com/Garyou19/LSTM_PyTorch_Electric-Load-Forecasting/HEAD/LSTM-pytorch.zip


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/LSTM-pytorch/best_model.pt:
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https://raw.githubusercontent.com/Garyou19/LSTM_PyTorch_Electric-Load-Forecasting/HEAD/LSTM-pytorch/best_model.pt


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/LSTM-pytorch/__pycache__/dataset_model.cpython-38.pyc:
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https://raw.githubusercontent.com/Garyou19/LSTM_PyTorch_Electric-Load-Forecasting/HEAD/LSTM-pytorch/__pycache__/dataset_model.cpython-38.pyc


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/LSTM-pytorch/process_data/dataset_split.py:
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 1 | import pandas as pd
 2 | from sklearn.model_selection import train_test_split
 3 | 
 4 | # 读取处理后的数据集
 5 | df = pd.read_csv('../dataset/processed_dataset.csv')
 6 | 
 7 | # 将数据集划分为训练集、验证集和测试集
 8 | train_ratio = 0.7
 9 | val_ratio = 0.2
10 | test_ratio = 0.1
11 | 
12 | train_data, test_data = train_test_split(df, test_size=test_ratio, shuffle=False)
13 | train_data, val_data = train_test_split(train_data, test_size=val_ratio/(train_ratio+val_ratio), shuffle=False)
14 | 
15 | # 保存划分后的数据集为CSV文件
16 | train_data.to_csv('../dataset/train_dataset.csv', index=False)
17 | val_data.to_csv('../dataset/val_dataset.csv', index=False)
18 | test_data.to_csv('../dataset/test_dataset.csv', index=False)
19 | print('数据划分已完成')


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/LSTM-pytorch/process_data/1.data_preparation.py:
--------------------------------------------------------------------------------
 1 | import pandas as pd
 2 | from sklearn.preprocessing import MinMaxScaler
 3 | 
 4 | # 读取数据集
 5 | df = pd.read_csv('../dataset/YearlyEliaGridLoadByQuarterHour_2022.csv')
 6 | 
 7 | # 处理缺失值
 8 | df.fillna(method='ffill', inplace=True)  # 使用前向填充方法填充缺失值
 9 | 
10 | # 去除逗号并将电力负荷值转换为浮点数
11 | df['Elia Grid Load [MW]'] = df['Elia Grid Load [MW]'].str.replace(',', '').astype(float)
12 | 
13 | # 将时间列转换为日期时间格式
14 | df['Datetime (CET+1/CEST +2)'] = pd.to_datetime(df['Datetime (CET+1/CEST +2)'], format='%d/%m/%Y %H:%M:%S')
15 | df['Datetime (UTC)'] = pd.to_datetime(df['Datetime (UTC)'], format='%d/%m/%Y %H:%M:%S')
16 | 
17 | # 按升序对数据进行排序
18 | df.sort_values('Datetime (UTC)', ascending=True, inplace=True)
19 | 
20 | # 只保留 Elia Grid Load 列
21 | df = df[['Elia Grid Load [MW]']]
22 | 
23 | # 数据归一化
24 | scaler = MinMaxScaler()
25 | df['Elia Grid Load [MW]'] = scaler.fit_transform(df[['Elia Grid Load [MW]']])
26 | 
27 | # 保存处理后的数据集
28 | df.to_csv('../dataset/processed_dataset.csv', index=False)
29 | print('Data Preparation Complete')


--------------------------------------------------------------------------------
/LSTM-pytorch/dataset_model.py:
--------------------------------------------------------------------------------
 1 | import torch
 2 | import torch.nn as nn
 3 | from torch.utils.data import DataLoader, Dataset
 4 | 
 5 | 
 6 | class TimeSeriesDataset(Dataset):
 7 |     def __init__(self, data, seq_length):
 8 |         self.data = data
 9 |         self.seq_length = seq_length
10 | 
11 |     def __len__(self):
12 |         return len(self.data) - self.seq_length
13 | 
14 |     def __getitem__(self, index):
15 |         x = self.data[index:index + self.seq_length]
16 |         y = self.data[index + self.seq_length]
17 |         return x, y
18 | 
19 | 
20 | class LSTMModel(nn.Module):
21 |     def __init__(self, input_size, hidden_size, num_layers, output_size):
22 |         super(LSTMModel, self).__init__()
23 |         self.hidden_size = hidden_size
24 |         self.num_layers = num_layers
25 |         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
26 |         self.fc = nn.Linear(hidden_size, output_size)
27 | 
28 |     def forward(self, x):
29 |         h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
30 |         c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
31 |         out, _ = self.lstm(x, (h0, c0))
32 |         out = self.fc(out[:, -1, :])
33 |         return out
34 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # 时间序列数据预处理和LSTM模型
 2 | 
 3 | ## data_preparation.py
 4 | 这个Python脚本用于对给定的数据集进行数据预处理和准备,以满足后续的数据分析、建模或机器学习任务的需求。
 5 | 
 6 | 功能和作用:
 7 | 
 8 | 1. **处理缺失值**: 使用前向填充的方式(ffill)填充缺失值,确保数据的连续性。
 9 | 2. **数据转换**: 将电力负荷列中的逗号去除,并将其转换为浮点数类型,以便后续处理。
10 | 3. **时间列处理**: 将时间列转换为日期时间格式,使其具有时间序列的性质,方便后续时间相关的分析和建模。
11 | 4. **数据排序**: 按照时间升序对数据进行排序,以确保数据按照时间顺序排列。
12 | 5. **保留所需列**: 只保留电力负荷列,去除其他不需要的列,以简化数据集并专注于感兴趣的特征。
13 | 6. **数据归一化**: 使用最小-最大缩放(MinMaxScaler)将电力负荷值归一化到0到1的范围内,以消除不同数值范围的影响,并使得数据更适合某些模型或算法。
14 | 7. **保存处理后的数据集**: 将经过处理和准备的数据保存为新的CSV文件,以便后续使用。
15 | 
16 | ## dataset_split.py
17 | 该脚本用于将数据集`YearlyEliaGridLoadByQuarterHour_2022`进行处理,划分为train、val、test三个数据集。
18 | 
19 | ## dataset_model.py
20 | 这个Python脚本定义了两个类:
21 | 
22 | ### TimeSeriesDataset
23 | 一个自定义的PyTorch数据集,用于处理时间序列数据。它接受输入数据和序列长度作为参数,并在`__getitem__`方法中返回按序列长度切片的输入和目标数据。这个类可以用于创建训练集和验证集的数据集对象。
24 | 
25 | ### LSTMModel
26 | 定义了一个LSTM模型的结构,继承自`nn.Module`。在`__init__`方法中定义了LSTM层和线性层,在`forward`方法中实现了模型的前向传播过程。
27 | 
28 | 这两个类的目的是为了在时间序列数据上构建和训练LSTM模型。`TimeSeriesDataset`类用于处理数据集的加载和处理,而`LSTMModel`类定义了LSTM模型的结构和前向传播方法。
29 | 
30 | ## train.py
31 | 该脚本用于训练LSTM模型,每一轮epoch打印训练loss和验证loss,最终保存训练完成后最好的模型`best.pt`。
32 | 
33 | ## test.py
34 | 该脚本用于在测试集上对已训练好的模型进行评估,主要功能包括:
35 | 
36 | 1. 加载已训练好的模型参数,使用`torch.load`函数加载已训练好的模型参数,将其应用到模型实例中。
37 | 2. 在测试集上进行预测,使用训练好的LSTM模型在测试集上进行预测,将预测结果存储在列表`predictions`中。
38 | 3. 将预测结果转换为一维数组,使用`np.concatenate`将列表中的预测结果连接为一个一维数组。
39 | 4. 绘制预测值和真实值的曲线图,使用`matplotlib.pyplot`绘制测试集中的真实值和预测值的曲线图,以便进行可视化对比。
40 | 


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/LSTM-pytorch/test.py:
--------------------------------------------------------------------------------
 1 | import torch
 2 | import torch.nn as nn
 3 | import numpy as np
 4 | import pandas as pd
 5 | import matplotlib.pyplot as plt
 6 | from torch.utils.data import DataLoader
 7 | from dataset_model import TimeSeriesDataset, LSTMModel
 8 | 
 9 | # 读取测试集的数据
10 | test_df = pd.read_csv('dataset/test_dataset.csv')
11 | 
12 | # 将数据集转换为PyTorch的Tensor
13 | test_data = torch.tensor(test_df['Elia Grid Load [MW]'].values, dtype=torch.float32).unsqueeze(1)
14 | 
15 | # 创建测试集的数据集对象
16 | seq_length = 10
17 | test_dataset = TimeSeriesDataset(test_data, seq_length)
18 | 
19 | # 创建数据加载器
20 | batch_size = 32
21 | test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
22 | 
23 | # 定义模型参数
24 | input_size = 1
25 | hidden_size = 64
26 | num_layers = 2
27 | output_size = 1
28 | 
29 | # 创建模型实例
30 | model = LSTMModel(input_size, hidden_size, num_layers, output_size)
31 | 
32 | # 设置设备
33 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
34 | 
35 | # 加载已训练好的模型参数
36 | model.load_state_dict(torch.load('best_model.pt'))
37 | model.to(device)
38 | model.eval()
39 | 
40 | # 在测试集上进行预测
41 | predictions = []
42 | 
43 | with torch.no_grad():
44 |     for inputs, targets in test_dataloader:
45 |         inputs = inputs.to(device)
46 | 
47 |         outputs = model(inputs)
48 |         predictions.append(outputs.detach().cpu().numpy())
49 | # # 预测值和真实值的对比
50 | # print("Predicted Values:")
51 | # print(predictions)
52 | # print("True Values:")
53 | # print(test_df['Elia Grid Load [MW]'].values[seq_length:])
54 | 
55 | # # 打印预测结果
56 | # print("Predictions:")
57 | # print(predictions)
58 | 
59 | # 将预测结果转换为一维数组
60 | predictions = np.concatenate(predictions).flatten()
61 | 
62 | # 绘制预测值和真实值的曲线图
63 | plt.plot(test_df.index, test_df['Elia Grid Load [MW]'], label='True Values')
64 | plt.plot(test_df.index[seq_length:], predictions, label='Predicted Values')
65 | plt.xlabel('Time')
66 | plt.ylabel('Load [MW]')
67 | plt.title('Predicted vs True Values')
68 | plt.legend()
69 | plt.show()


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/LSTM-pytorch/train.py:
--------------------------------------------------------------------------------
 1 | import torch
 2 | import torch.nn as nn
 3 | import pandas as pd
 4 | import numpy as np
 5 | from torch.utils.data import DataLoader
 6 | from dataset_model import TimeSeriesDataset, LSTMModel
 7 | 
 8 | # 读取训练集和验证集的数据
 9 | train_df = pd.read_csv('dataset/train_dataset.csv')
10 | val_df = pd.read_csv('dataset/val_dataset.csv')
11 | 
12 | # 将数据集转换为PyTorch的Tensor
13 | train_data = torch.tensor(train_df['Elia Grid Load [MW]'].values, dtype=torch.float32).unsqueeze(1)
14 | val_data = torch.tensor(val_df['Elia Grid Load [MW]'].values, dtype=torch.float32).unsqueeze(1)
15 | 
16 | # 创建训练集和验证集的数据集对象
17 | seq_length = 10
18 | train_dataset = TimeSeriesDataset(train_data, seq_length)
19 | val_dataset = TimeSeriesDataset(val_data, seq_length)
20 | 
21 | # 创建数据加载器
22 | batch_size = 32
23 | train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
24 | val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
25 | 
26 | # 定义模型参数
27 | input_size = 1
28 | hidden_size = 64
29 | num_layers = 2
30 | output_size = 1
31 | 
32 | # 创建模型实例
33 | model = LSTMModel(input_size, hidden_size, num_layers, output_size)
34 | 
35 | # 设置训练设备
36 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
37 | 
38 | # 将模型移动到训练设备
39 | model.to(device)
40 | 
41 | # 定义训练参数
42 | num_epochs = 10
43 | learning_rate = 0.001
44 | 
45 | # 定义损失函数和优化器
46 | criterion = nn.MSELoss()
47 | optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
48 | 
49 | # 初始化最好的验证集损失
50 | best_val_loss = float('inf')
51 | 
52 | # 训练模型
53 | for epoch in range(num_epochs):
54 |     model.train()  # 设置模型为训练模式
55 |     train_loss = 0.0
56 | 
57 |     for i, (inputs, targets) in enumerate(train_dataloader):
58 |         inputs = inputs.to(device)
59 |         targets = targets.to(device)
60 | 
61 |         optimizer.zero_grad()
62 |         outputs = model(inputs)
63 |         loss = criterion(outputs, targets)
64 |         loss.backward()
65 |         optimizer.step()
66 | 
67 |         train_loss += loss.item()
68 | 
69 |     train_loss /= len(train_dataloader)
70 | 
71 |     # 在验证集上进行评估
72 |     model.eval()  # 设置模型为评估模式
73 |     val_loss = 0.0
74 | 
75 |     with torch.no_grad():
76 |         for inputs, targets in val_dataloader:
77 |             inputs = inputs.to(device)
78 |             targets = targets.to(device)
79 | 
80 |             outputs = model(inputs)
81 |             loss = criterion(outputs, targets)
82 | 
83 |             val_loss += loss.item()
84 | 
85 |         val_loss /= len(val_dataloader)
86 | 
87 |     # 打印训练结果
88 |     print(f'Epoch [{epoch+1}/{num_epochs}], Train Loss: {train_loss:.6f}], Val Loss: {val_loss:.6f}')
89 | 
90 |     # 保存最好的模型
91 |     if val_loss < best_val_loss:
92 |         best_val_loss = val_loss
93 |         torch.save(model.state_dict(), 'best_model.pt')
94 | 
95 | print("训练结束了.")


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/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | share/python-wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | MANIFEST
 28 | 
 29 | # PyInstaller
 30 | #  Usually these files are written by a python script from a template
 31 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 32 | *.manifest
 33 | *.spec
 34 | 
 35 | # Installer logs
 36 | pip-log.txt
 37 | pip-delete-this-directory.txt
 38 | 
 39 | # Unit test / coverage reports
 40 | htmlcov/
 41 | .tox/
 42 | .nox/
 43 | .coverage
 44 | .coverage.*
 45 | .cache
 46 | nosetests.xml
 47 | coverage.xml
 48 | *.cover
 49 | *.py,cover
 50 | .hypothesis/
 51 | .pytest_cache/
 52 | cover/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | .pybuilder/
 76 | target/
 77 | 
 78 | # Jupyter Notebook
 79 | .ipynb_checkpoints
 80 | 
 81 | # IPython
 82 | profile_default/
 83 | ipython_config.py
 84 | 
 85 | # pyenv
 86 | #   For a library or package, you might want to ignore these files since the code is
 87 | #   intended to run in multiple environments; otherwise, check them in:
 88 | # .python-version
 89 | 
 90 | # pipenv
 91 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 92 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 93 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 94 | #   install all needed dependencies.
 95 | #Pipfile.lock
 96 | 
 97 | # poetry
 98 | #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
 99 | #   This is especially recommended for binary packages to ensure reproducibility, and is more
100 | #   commonly ignored for libraries.
101 | #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
103 | 
104 | # pdm
105 | #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106 | #pdm.lock
107 | #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | #   in version control.
109 | #   https://pdm.fming.dev/#use-with-ide
110 | .pdm.toml
111 | 
112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113 | __pypackages__/
114 | 
115 | # Celery stuff
116 | celerybeat-schedule
117 | celerybeat.pid
118 | 
119 | # SageMath parsed files
120 | *.sage.py
121 | 
122 | # Environments
123 | .env
124 | .venv
125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
130 | 
131 | # Spyder project settings
132 | .spyderproject
133 | .spyproject
134 | 
135 | # Rope project settings
136 | .ropeproject
137 | 
138 | # mkdocs documentation
139 | /site
140 | 
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 | 
146 | # Pyre type checker
147 | .pyre/
148 | 
149 | # pytype static type analyzer
150 | .pytype/
151 | 
152 | # Cython debug symbols
153 | cython_debug/
154 | 
155 | # PyCharm
156 | #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | #  and can be added to the global gitignore or merged into this file.  For a more nuclear
159 | #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 | 


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/LSTM-pytorch/dataset/test_dataset.csv:
--------------------------------------------------------------------------------
   1 | Elia Grid Load [MW]
   2 | 0.332080840273371
   3 | 0.3489155425051827
   4 | 0.359102538195751
   5 | 0.3733183480270736
   6 | 0.38528860604316
   7 | 0.42202429957583
   8 | 0.4556423952103871
   9 | 0.479509977221124
  10 | 0.5017495494666586
  11 | 0.518531616796287
  12 | 0.5376256416026268
  13 | 0.5340150505218557
  14 | 0.5302973535354794
  15 | 0.5371538655898409
  16 | 0.5362637645114476
  17 | 0.5289279280280956
  18 | 0.5167493745270126
  19 | 0.5126057020531998
  20 | 0.4806006213570604
  21 | 0.4680930997173322
  22 | 0.4497749296900733
  23 | 0.4503072970437444
  24 | 0.4205409337860071
  25 | 0.3988977884262583
  26 | 0.3809343939606142
  27 | 0.371717267746709
  28 | 0.3768967053305386
  29 | 0.3704001711246354
  30 | 0.3649101264145503
  31 | 0.3528877435244025
  32 | 0.3416096956754001
  33 | 0.3422878290663191
  34 | 0.3510210405981365
  35 | 0.3608499965471096
  36 | 0.3722457588866535
  37 | 0.3869129436058845
  38 | 0.3866510951680854
  39 | 0.3990612014365248
  40 | 0.4130549450235587
  41 | 0.430886854239369
  42 | 0.4460392797138049
  43 | 0.4679413153482457
  44 | 0.4810261888219992
  45 | 0.4986093063199319
  46 | 0.5219489072188463
  47 | 0.535783828048711
  48 | 0.5490564938769595
  49 | 0.5640859005683856
  50 | 0.5756578266212442
  51 | 0.597925960441607
  52 | 0.6129677098135837
  53 | 0.6310495328091401
  54 | 0.6263584981548713
  55 | 0.6293581775511938
  56 | 0.6209167021051207
  57 | 0.6071284998512247
  58 | 0.6084752801099949
  59 | 0.5810682049386839
  60 | 0.5721714783909755
  61 | 0.5730129227865366
  62 | 0.5536632718854853
  63 | 0.5295839261985227
  64 | 0.5304307768921763
  65 | 0.5124293343286364
  66 | 0.510452363322409
  67 | 0.5169939840142905
  68 | 0.4979234205015596
  69 | 0.4919911814097605
  70 | 0.4721143654418817
  71 | 0.4741839575550516
  72 | 0.4894674602568237
  73 | 0.4747162229030985
  74 | 0.4653629191806378
  75 | 0.4700582380711308
  76 | 0.4549782365900092
  77 | 0.4612315893873757
  78 | 0.4473166961479922
  79 | 0.4386798799312196
  80 | 0.4309791693294385
  81 | 0.4329379833345251
  82 | 0.4184467583173601
  83 | 0.3987503902990203
  84 | 0.3875410942408543
  85 | 0.3702247214506913
  86 | 0.3821779445275053
  87 | 0.3618075233432221
  88 | 0.3633752477844124
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