tsyet12 / DAZLS

Domain adaptation zero shot learning for near real-time prediction of renewable energy

DAZLS

Domain adaptation zero shot learning for near real-time prediction of renewable energy

This repository demonstrates the main algorithm DAZLS in the most basic implementation. It compliments the paper (below) to demonstrate the basic DAZLS algorithm:

S.Y. Teng et al. (2023). Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence. Renewable and Sustainable Energy Reviews.

Instructions

1. The "combined_data" folder contains the raw data. (Example data is provided)
2. Use PrepData.py to preprocess data from "combined_data" and save it in "prep_data" folder.
3. After running PrepData.py the preprocessed data with metadata can be found in prep_data folder.
4. Run Model.py to test the DAZLS model.

Acknowledgements

The research contribution from S.Y. Teng is supported by the European Union's Horizon Europe Research and Innovation Program, under Marie Skłodowska-Curie Actions grant agreement no. 101064585 (MoCEGS).