Predicting Photovoltaic Solar Energy Generation using Capsule Network Architecture

Abdo A., DANDIL E.

8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024, Malatya, Türkiye, 21 - 22 Eylül 2024 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/idap64064.2024.10710759
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: capsule network, photovoltaic systems, Solar energy generation, solar energy prediction, unisolar dataset
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

The growing reliance on photovoltaic (PV) solar energy as a sustainable source of electricity requires accurate forecasting models to ensure efficient integration into the grid. Traditional methods, including statistical approaches and conventional deep learning models, often struggle with the inherent variability and complex dependencies in solar energy data. This study proposes an approach to predicting photovoltaic solar energy generation using a Capsule Network (CapsNet) architecture. In the study, solar energy generation data from the publicly available UNISOLAR dataset are used. Through extensive experimentation, we demonstrate that CapsNet outperforms traditional machine learning models in prediction of solar energy generation, with R^2=0.94, R M S E=1.95 and MAE=0.97. Among the various techniques, CapsNets shows particular promise for capturing complex relationships in solar data and providing highly accurate predictions. The results underscore the potential of CapsNets in enhancing the reliability and efficiency of solar energy predicting, contributing to more effective energy management and grid stability.