An Innovative Hybrid Deep Learning Approach for Enhanced Electrical Power Prediction Using Meteorological Data: GGWO-IEMD/SCPDAE-LSTM-SDPAM/RS Model

ÖZBAY KARAKUŞ M., ŞAHİN M. E., ULUTAŞ H.

Arabian Journal for Science and Engineering, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s13369-024-09486-5
  • Dergi Adı: Arabian Journal for Science and Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, Pollution Abstracts, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Climate factors, GGWO, Hybrid deep IEMD/SCPDAE, Hybrid deep model, Hydroelectric generation
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

Accurate forecasting of renewable energy generation is vital for efficient resource management. This study introduces an innovative approach that combines deep learning techniques, feature selection, noise reduction, and optimization algorithms to enhance short- and long-term power predictions using meteorological data. The proposed GGWO-IEMD/SCPDAE-LSTM-SDPAM/RS model integrates multiple components to capture spatial and temporal correlations, making it highly effective in predicting power production. Experimentation on the Hirfanlı Hydropower Plant’s data spanning 2007–2021 demonstrates the model’s superiority in terms of accuracy, robustness, and efficiency. Results demonstrate that the GGWO-IEMD/SCPDAE-LSTM-SDPAM/RS model outperforms other examined models in terms of metrics such as R (0.994), RMSE (91 kWh), and MAE (128 kWh), highlighting its performance. Furthermore, comparative analysis across various prediction models highlights the superior performance of the proposed model, particularly in one-day-ahead and one-year-ahead predictions. Beyond energy management, this hybrid approach holds promise for diverse applications, including early warning systems, showcasing its potential in addressing complex real-world challenges and advancing accurate energy production prediction systems.