Year: 2024
Type: article
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.
Authors: An Duy Huynh, Thanh Khuong Nguyen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403082376