Investigating the Power of LSTM-Based Models in Solar Energy Forecasting

• Main Authors: Jailani N.L.M., Dhanasegaran J.K., Alkawsi G., Alkahtani A.A., Phing C.C., Baashar Y., Capretz L.F., Al-Shetwi A.Q., Tiong S.K.
• Other Authors: 58297401800
• Format: Review
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2024
• Subjects: deep learning; hybrid model; long short-term memory; photovoltaic power forecasting; renewable energy; solar irradiance forecasting; Brain; Global warming; Learning systems; Long short-term memory; Mean square error; Natural resources; Solar energy; Solar power generation; Solar radiation; Deep learning; Hybrid model; Photovoltaic power; Photovoltaic power forecasting; Power; Power forecasting; Renewable energies; Renewable energy source; Solar irradiance forecasting; Solar irradiances; Forecasting

Solar is a significant renewable energy source. Solar energy can provide for the world�s energy needs while minimizing global warming from traditional sources. Forecasting the output of renewable energy has a considerable impact on decisions about the operation and management of power systems. It is...