Solar Irradiance Prediction Using Grey Wolf Optimized LSTM (GWO-LSTM)
Abstract
Accurate prediction of solar irradiance is required for integrating solar energy into power grids and energy storage systems to improve efficiency and effectiveness. This paper represents a hybrid model which combines Grey Wolf Optimization (GWO) and Long Short Term Memory (LSTM). In order to increase prediction accuracy and lessen reliance on manual training, GWO is utilized to optimize the LSTM's parameters. The proposed model demonstrates improved accuracy when compared to traditional methods like normal LSTM.
Fields of Study & Subjects
Solar Radiation and Photovoltaics
Energy Load and Power Forecasting
Solar Thermal and Photovoltaic Systems
Publication details
- Journal / source
- 2026 9th International Conference on Intelligent Computing and Control Systems (ICICCS)
- Publisher
- IEEE
- Pages
- 490–494
- Publication date
- 16 March 2026