Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates

Guillermo Almonacid-Olleros, Gabino Almonacid, David Gil and Javier Medina-Quero
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Guillermo Almonacid-Olleros: Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Gabino Almonacid: Department of Electronic Engineering, University of Jaén, 23071 Jaén, Spain
David Gil: Department of Computer Science Technology and Computation, University of Alicante, 03690 Alicante, Spain
Javier Medina-Quero: Department of Computer Science, University of Jaén, 23071 Jaén, Spain

Sustainability, 2022, vol. 14, issue 5, 1-15

Abstract: New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.

Keywords: photovoltaic systems; deep learning; transfer learning; energy generation; distributed generation; smart-grids (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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