Forecasting Solar Radiation Using Long Short-Term Memory Neuron Network in Thailand - Kiratipongwut, Pasin; Pattarapanitchai, Somjet; Srimueang, Chaninat; Warunsin, Kulwarun; Buntoung, Sumaman (Journal of Advanced Development in Engineering and Science, 2026)
Solar energy is an important alternative energynowadays, as it helps reduce carbon emissions. Solar energy can be converted into electricity or thermal energy using specialized systems. Common types of solar energy systems include photovoltaic systems, solar thermal systems, and solar drying systems. To evaluate and optimize the performance of these systems, it is essential to know the amount of incident solar energy received by the devices. For this reason, this study aimsto forecast solar radiation at 1 hour 2 hours 3 hours 4 hours and 24 hours ahead using a Long Short-Term Memory (LSTM) machine learning model. Ground-based solar radiation dataand several atmospheric parameters from ERA5 at Nakhon Pathom site (13.82ºN, 100.04ºE), Thailand, were used for the period from 2019 to 2023.The results show that the developed LSTM model performs best for 1-hour ahead forecasting, achieving a normalized root mean square error (nRMSE) of 20.93% and a normalized mean bias error (nMBE) of 0.12%, compared to the measured data.