https://github.com/avinash793/adversarial-attacks-on-load-forecasting-model

Studied the impact of adversarial attacks on RNN Based load forecasting model.
https://github.com/avinash793/adversarial-attacks-on-load-forecasting-model

adversarial-attacks adversarial-examples adversarial-learning adversarial-machine-learning deep-learning forecasting forecasting-models keras load-forecasting python3 rnn rnn-lstm security

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Studied the impact of adversarial attacks on RNN Based load forecasting model.

• Host: GitHub
• URL: https://github.com/avinash793/adversarial-attacks-on-load-forecasting-model
• Owner: Avinash793
• Created: 2024-01-14T18:13:58.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-01-15T20:49:13.000Z (10 months ago)
• Last Synced: 2024-01-28T00:08:54.638Z (9 months ago)
• Topics: adversarial-attacks, adversarial-examples, adversarial-learning, adversarial-machine-learning, deep-learning, forecasting, forecasting-models, keras, load-forecasting, python3, rnn, rnn-lstm, security
• Language: Jupyter Notebook
• Homepage:
• Size: 2.75 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Adversarial Attacks on Load Forecasting Model

## Dataset Information

### Dataset Used

1. ENTSO-E Dataset (European Network of Transmission System Operators for Electricity) for hourly load data in Switzerland.

2. DarkSky Dataset for hourly temperature and weather Icon information of 8 major cities in Switzerland.

### Dataset Raw Features

There are 21 raw features at each timestamp:

1. **Load**

2. **8 Cities temperature**

3. **8 Cities weather Icon Information** - categorical feature tells which weather icon [categories: icon1, icon2, icon3]

4. **Holiday** - boolean feature tells weather holiday in switzerland on that date

5. **Month** - categorical feature tells data of which month  [categories: Jan, Feb, ... , Dec]

6. **Day** - categorical feature tells data of which day [categories: Mon, Tues, Wed, Thrus, Fri, Sat, Sun]

7. **Hour** - categorical feature tells data of which hour  [categories: 0, 1, 2, ... , 23]

### Dataset Source

You can use already preprocessed data present in `data` folder with name `actual_dataset.csv` .

**Feature Vector 77 dimensional at each timestamp:** \

actual_load - 1 feature \

8 cities temperature - 8 features \

8 cities weather icon one hot encoding - (8 cities x 3 categories of icon) = 24 features \

holiday - 1 feature \

weekday one hot encoding  - 7 features \

hour one hot encoding  - 24 features \

month one hot encoding  - 12 features 

**NOTE:** Please ignore `entsoe` feature column in `actual_dataset.csv`. 

## Train Load Forecasting Model

1. change `DATASET_SPLIT_DATE` in `constants.py` according to how you want to split train and test dataset.

2. Simply Run

    ```shell

    python forecasting.py

    ```

3. It will save trained model weights in `output/load_forecasting_model_weights.h5`. save `output/loss_epoch_curve.png` and `output/actual_predicted_load.png` images.

## Generate Adversarial Datasets

1. Simply Run:

    ```shell

    python adversarial.py

    ```

2. It will generate adversarial datasets for various temperature variation in `data` folder. For Ex: `adversarial_dataset_temp_1.csv` means generate adversarial temperature dataset with 1 Fahrenheit change in temperature.

## Results

Check `results.ipynb` file to see various plots like:

1. Temperature Profile

2. Load Forecasting Profile

3. Forecasting MAPE with Temperature Variation