Power_Forecasting

Time Series Analysis and User Behaviour Learning to forecast power usage in a household.

Dataset

The Dataset used to test the performance of various models for the task at hand are GreenD and UkDale. The resampled files of these datasets are present in the Data folder. The models can be used for any new data, provided they are available in the given format.

Using

Initialisation

• Clone the repository

git clone https://github.com/Home-Electricity-Manager/Power_Forecasting.git

• Install the dependencies

pip install --r requirements.txt

Prepare Data

• Preprocess the data to equipment-wise usable format

python Data/preprocess.py --src_dir <path-to-directory-containing-data> --src_file_name \
       <common-prefix-for-all-equipments> --tar_dir <target-directory-to-place-preprocessed-data>\
       --num_equips <number-of-equipments> --n_steps <number-of-timesteps, default=128> 

Run the Models

• ARIMA

python Models/ARIMA.py --src_dir <path-to-directory-containing-preprocessed-data> --tar_dir \
      <path-where-the-outputs-need-to-be-placed> --eq_num <equipment-number-to-be-tested> \
      --num_preds <number-of-predictions-to-be-made> --draw_graphs <bool-variable-whether-to-draw-graphs-or-not>

• CNN_LSTM

python Models/CNN_LSTM.py --src_dir <path-to-directory-containing-preprocessed-data> --tgt_dir \
       <path-where-the-outputs-need-to-be-placed> --eq_num <equipment-number-to-be-tested> --n_test \
       <number-of-predictions-to-be-made> --n_models <numer-of-CNN_LSTM-models-to-be-made> \
       --draw_graphs <bool-variable--whether-to-draw-graphs-or-not>

• Ensemble

python Models/Ensemble.py --src_dir <path-to-directory-containing-preprocessed-data> --tgt_dir \
       <path-where-the-outputs-need-to-be-placed> --eq_num <equipment-number-to-be-tested> \
       --n_test <number-of-predictions-to-be-made> --n_models <numer-of-CNN_LSTM-models-to-be-made>\
       --draw_graphs <bool-variable--whether-to-draw-graphs-or-not>

Finding Start-End Time

python Models/StartFinish.py --data_path <path-to-directory-containing-data-to-apply-on> --target_path <path-where-the-outputs-need-to-be-placed> --num_preds <number-of-predictions-to-be-worked-on>