/adversarial-attacks-on-load-forecasting-model

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

Primary LanguageJupyter Notebook

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 
    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: 
    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