LSTM-MultiStep-Forecasting

Implementation of Electric Load Forecasting Based on LSTM (BiLSTM). Including direct-multi-output forecasting, single-step-scrolling forecasting, multi-model-single-step forecasting, multi-model-scrolling forecasting, and seq2seq forecasting.

Environment

pytorch==1.10.1+cu111

numpy==1.18.5

pandas==1.2.3

Tree

.
│  args.py
│  data_process.py
│  models.py
│  model_test.py
│  model_train.py
│  README.md
│  tree.txt
│  
├─.idea
│  │  LSTM-MultiStep-Forecasting.iml
│  │  misc.xml
│  │  modules.xml
│  │  other.xml
│  │  vcs.xml
│  │  workspace.xml
│  │  
│  └─inspectionProfiles
│          profiles_settings.xml
│          Project_Default.xml
│          
├─algorithms
│      multiple_outputs.py
│      multi_model_scrolling.py
│      multi_model_single_step.py
│      seq2seq.py
│      single_step_scrolling.py
│      
├─data
│      data.csv
│      
└─models
    │  multiple_outputs.pkl
    │  seq2seq.pkl
    │  single_step_scrolling.pkl
    │  
    ├─mms
    │      0.pkl
    │      1.pkl
    │      10.pkl
    │      11.pkl
    │      2.pkl
    │      3.pkl
    │      4.pkl
    │      5.pkl
    │      6.pkl
    │      7.pkl
    │      8.pkl
    │      9.pkl
    │      
    └─mmss
            0.pkl
            1.pkl
            10.pkl
            11.pkl
            2.pkl
            3.pkl
            4.pkl
            5.pkl
            6.pkl
            7.pkl
            8.pkl
            9.pkl

1. args.py is a parameter configuration file, where you can set model parameters and training parameters.
2. data_process.py is the data processing file. If you need to use your own data, then you can modify the load_data function in data_process.py.
3. Three models are defined in models.py, including LSTM, bidirectional LSTM, and seq2seq.
4. model_train.py defines the training functions of the models in the five multi-step prediction methods.
5. model_test.py defines the testing functions of the models in the five multi-step prediction methods.
6. The trained model is saved in the models folder, which can be used directly for testing. The mms folder saves the model of multi-model-scrolling forecasting, and the mmss folder saves the model of multi-model-single-step forecasting.
7. Data files in csv format are saved under the data file.

Usage

First switch the working path:

cd algorithms/

Then, execute in sequence:

python multi_model_scrolling.py --epochs 50 batch_size 30
python multi_model_single_step.py --epochs 50 batch_size 30
python multiple_outputs.py --epochs 50 batch_size 30
python seq2seq.py --epochs 50 batch_size 30
python single_step_scrolling.py --epochs 50 batch_size 30

If you need to change the parameters, please modify them manually in args.py.

Result

Predict the next 12 steps, epochs=50, bacth_size=30, and the results of the 5 methods are shown in the following table:

GitHub

View Github