Probabilistic-Wind-Power-Forecasting-An-Adaptive-Federated-Approach

Codes for the paper "Probabilistic Wind Power Forecasting: An Adaptive Federated Approach".

Authors: Xiaorong Wang, Yangze Zhou

Requirements

The must-have packages can be installed by running

pip install requirements.txt

conda env create -f environment.yml

Experiments

There are five forecasting settings in this work and the code for these settings is organized in the same way. The results and models are saved in https://drive.google.com/drive/folders/17qs0H3TlKMRQcyTvJHz3r-gSU_MO5KGS?usp=drive_link.

Data

All the clean data for experiments are saved in Data/GFC12.

The row data can be found in Data/GFC12 row.

You can also find the code for processing the data in this fold.

Reproduction

If you want to run the proposed approach, you can run test.ipynb.

If you want to reproduct the result of benchmarks, you can run main.ipynb.

If you want to find the result of the table in the paper, you can refer to analysis.ipynb.