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The Comparison of GRU and LSTM in Solar Power Generation Forecasting Application
Department of Mechatronics and Automation Engineering, Faculty of Electrical & Electronics Engineering, Ly Tu Trong College, Ho Chi Minh City, Viet Nam.
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 1360–1370.
Article DOI: 10.30574/ijsra.2024.13.1.1831
Publication history:
Received on 10 August 2024; revised on 26 September 2024; accepted on 28 September 2024
Abstract:
Precise forecasting of solar power generation is essential for optimizing the incorporation of renewable energy into the electrical grid and maintaining the stability of energy systems. This paper offers a thorough comparative examination of Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks in forecasting solar power generation. Both models were assessed for predicted accuracy and computational efficiency, employing essential performance indicators including Mean Absolute Error (MAE) and R-squared (R²). The efficacy of LSTM and GRU in forecasting is assessed using a real-world dataset. Comprehensive experimental data demonstrate that the GRU model surpasses the LSTM model.
Keywords:
Solar power forecasting; LSTM; GRU; Deep learning; Renewable energy
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
