In this paper, we analysed the impact of using optimum combination of input variables and low dimensional subspace on Photovoltaic (PV) production forecasting accuracy. We worked in collaboration with Prof. Mussetta from Politecnico di Milano.
- Optimum combination of input meteorological features using feature extraction technique
- Low dimensional subspace using dimensional reduction technique
To support reproducibility and validating the results we have released the dataset utilized in the work along with the codes.
Github repository with used dataset and evaluation code
For more information please see the paper:
Ekanki Sharma, Marco Mussetta, and Wilfried Elmenreich. Investigating the impact of data quality on the energy yield forecast using data mining techniques. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, October 2020.