Madden-Julian Oscillation Based Subseasonal Forecasting for Renewable Energy Applications in Tropical Indonesia: Establishing Operational Baseline Performance

Operational subseasonal forecasting for renewable energy applications in tropical regions remains an underexplored challenge critical for developing countries' energy transitions. This study establishes baseline performance for Madden-Julian Oscillation (MJO) based renewable energy forecasting in tropical Indonesia, addressing a significant gap in operational meteorological applications. Using West Java as a representative tropical archipelagic region, we develop a comprehensive forecasting system integrating 25 years of meteorological observations with real-time MJO monitoring. MJO composite analysis reveals systematic solar radiation variability (12.08 W/m² range) with clear operational implications: enhanced conditions during Phase 4 (+ 7.26 W/m²) and suppressed conditions during Phase 7 (-4.82 W/m²). A hybrid CNN-LSTM model processes atmospheric patterns and MJO evolution for 14-day renewable energy predictions. While overall skill remains modest (ACC = 0.202), conditional performance during organized MJO periods achieves near-operational capability (ACC = 0.64), providing viable forecasting windows for 28% of time periods. Seasonal analysis identifies optimal deployment during December-February monsoon conditions. Economic analysis demonstrates positive return for installations > 50 MW during high-skill periods through optimized maintenance and grid management. This baseline establishment provides crucial foundation for operational meteorological services in tropical developing regions, supporting renewable energy sector growth through adaptive forecasting strategies.