Stride-TCN for energy consumption forecasting and its optimization

Link to Paper : Appl. Sci. 2022

Requirements

environment.yml: Installation the list of libraries

Structure of repository

.
├── dataset
│   ├── Electricity data_CNU
│   ├── Household_power_consumption
│   └── Spain_Energy_Consumption
├── auto_correlation
│   ├── auto_cnu_stride.py
│   ├── auto_househole_stride.py
│   ├── auto_spain_stride.py
│   ├── auto_stride_searching.py
│   ├── models.py
│   ├── run_train_auto_stride.sh
│   ├── run_train_manual_stride.sh
│   ├── stride_cnu_manual.py
│   ├── stride_househole_manual.py
│   ├── stride_spain_manual.py
│   └── test.py
├── automl_searching
│   ├── exp_gru_cnu.py
│   ├── exp_gru_househole.py
│   ├── exp_gru_spain.py
│   ├── exp_lstm_cnu.py
│   ├── exp_lstm_househole.py
│   ├── exp_lstm_spain.py
│   ├── exp_searching_models_cnu.py
│   ├── exp_searching_models_househole.py
│   └── exp_searching_models_spain.py
├── cnu_power_consumption.ipynb
├── config.yaml
├── count_params_model.py
├── environment.yml
├── ett_electricity_transformer_temperature.ipynb
├── export_mse_mae.py
├── export_params.py
├── extract_stride_result.py
├── fedot_test.py
├── household_electric_power_consumption.ipynb
├── keras_auto.ipynb
├── run.sh
├── searching_models_dbs.ipynb
├── show_compare_errors.py
├── spain_energy_consumption.ipynb
├── utils.py
└── visualize.py

Detailed Description

• visualize.py: automatically read all log files, then visualize the results(LSTM, GRU, TCN, Stride TCN)

• export_mse_mae.py and export_params.py to get the table comparison, following the bellow

                              1 hours   12 hours  ...  72 hours  84 hours
auto-tcn                      23681.0  1495436.0  ...  650696.0   11716.0
lstm                         372351.0   374012.0  ...  383072.0  384884.0
gru                          103747.0   104462.0  ...  108362.0  109142.0
Autocorrelation-Dilated TCN    3521.0     3884.0  ...    5864.0    6260.0
auto-stride-2layers            2081.0    30540.0  ...   34440.0    3492.0
auto-stride-3layers           58817.0    59532.0  ...   63432.0   64212.0
auto-stride-4layers           87809.0     1268.0  ...    1808.0    2316.0

./plot-comparison:

plot_comparison.py: plot the comparison size of models and errors

./auto_correlation: (our proposed model here)

run_train_auto_stride.sh and run_train_manual_strie.sh is scripts to run Stride-TCN automatically and Stride-TCN heuristic respectively

./automl_searching:

• To run LSTM experiment on 3 datasets: exp_lstm_househole.py exp_lstm_cnu.py exp_lstm_spain.py

• To run GRU experiment on 3 datasets: exp_gru_househole.py exp_searching_models_househole.py exp_searching_models_spain.py

• To run TCN automatically searching HO experiment on 3 datasets: exp_searching_models_cnu.py exp_gru_cnu.py exp_gru_spain.py

Datasets

This repository is using 3 datasets:

• Individual household electric power consumption is available online
• The energy consumption curves of 499 customers from Spain is available online
• The CNU energy consumption is available online

TIPS:

Run background with specified name

bash -c "exec -a Andrew python exp_lstm_cnu.py &"