A Hybrid GRU-BiLSTM Deep Learning Framework for Solar Radiation Forecasting and Photovoltaic Energy Yield Assessment in Timor Island, Indonesia - Yobel Eliezer Mahardika; Agustina Rachmawardani; Marzuki Sinambela (INFOKUM, 2026)
Timor Island in East Nusa Tenggara possesses abundant solar energy resources, yet the technical feasibility of solar power plant (PLTS) development in the region has rarely been assessed using data-driven quantitative methods. This study aims to develop and validate a hybrid GRU-BiLSTM deep learning model for short-term solar radiation forecasting, to assess whether this model can be reliably extended into long-horizon autoregressive projection, to estimate the solar radiation potential and photovoltaic energy production across five locations in Timor Island, and to characterize the radiation variability relevant to PLTS design. A hybrid GRU-BiLSTM deep learning model was developed and evaluated for short-term hourly solar radiation forecasting using ERA5 reanalysis data (2015-2025), achieving R² of 0.9507-0.9584 across five locations. Because chained autoregressive projection using this model was found to be unreliable for annual-horizon estimation (R² = -0.53, +46% overestimation), a climatological approach based on 2015-2025 historical averages was applied instead for potential assessment. The results show that the five locations possess high and relatively uniform solar potential, with annual totals ranging from 2,023 to 2,109 kWh/m² (5.54-5.78 kWh/m²/day), corresponding to estimated photovoltaic energy production of 273.1-284.8 kWh/m² per year. Coefficient of variation values (69.6-71.0%) indicate substantial short-term fluctuation despite the locations' consistent seasonal pattern, with the lowest production occurring in June and the highest in October. These findings provide a quantitative basis for PLTS capacity planning and energy storage design in Timor Island, demonstrating that a validated short-term forecasting model can be meaningfully extended into practical renewable energy resource assessment.