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.
The main contribution presented in the paper is divided in two parts:
- Optimum combination of input meteorological features using feature extraction technique
- Low dimensional subspace using dimensional reduction technique
We assess and compare two cases when forecasting models are fed with all the features with the case when low subspace of dataset is used as an input to the models.
The simulation results reveal that depending on the location under study and the regression methods, using less variables as input to the forecasting models are enough to generate nearly similar results without affecting the performance. However, it is necessary to conduct the tests under different climatic conditions so as to ensure the reliability of the results.
The figures below show the results obtained applying Pearsons correlation and principal component analysis. Figure 1 represents strength of association between two variables. Figure 2 is the biplot representation of the input features contributing variance on principal components PC1 and PC2.
Fig.1 : Pearson correlation map
Fig.2 : Biplot representation
ISGT Europe 2020 was held virtually and we recorded the presentation for the same. The presentation is available at this link.
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.
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