Boosting Solar Power Forecast Accuracy: Deep Learning and Explainable Artificial Intelligence Integration

Ozdemir G., ÖZDEMİR U., Kuzlu M.

2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024, Ankara, Türkiye, 16 - 18 Ekim 2024, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/asyu62119.2024.10757008
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: deep learning, forecasting, RNN, solar power generation, XAI
  • Erciyes Üniversitesi Adresli: Evet

Özet

Accurate forecasting of solar power generation is very important for integrating renewable energy into the smart grid and ensuring energy reliability. This study uses a Recurrent Neural Network structure to improve the accuracy of solar power generation forecasts. To improve the reliability and transparency of forecasts, the Local Interpretable Model-agnostic Explanation has been used as an Explainable Artificial Intelligence method. Model performance has been evaluated using common metrics such as Mean Square Error, Mean Absolute Error and Root Mean Squared Error. The application of Local Interpretable Model-agnostic Explanation has provided valuable insights into the model's decision-making process by identifying the meteorological features that are most effective in generating solar power. This integration of deep learning and Explainable Artificial Intelligence method not only achieved high forecast accuracy but also made the forecasts more reliable and transparent.