Madden-Julian Oscillation Based Subseasonal Forecasting for Renewable Energy Applications in Tropical Indonesia: Establishing Operational Baseline Performance
DOI:
https://doi.org/10.62710/y2e1rp18Keywords:
Madden-julian oscillation Subseasonal forecasting Renewable energy Tropical meteorologyAbstract
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.
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Ahn, M.-S., Kim, D., Ham, Y.-G., & Park, S. (2020). Role of Maritime Continent Land Convection on the Mean State and MJO Propagation. Journal of Climate, 33(5), 1659–1675.
https://doi.org/10.1175/JCLI-D-19-0342.1
Archer, C. L., & Jacobson, M. Z. (2005). Evaluation of global wind power. Journal of Geophysical Research: Atmospheres, 110(D12), 1–20. https://doi.org/10.1029/2004JD005462
Baranowski, D. B., Waliser, D. E., Jiang, X., Ridout, J. A., & Flatau, M. K. (2019). Contemporary GCM Fidelity in Representing the Diurnal Cycle of Precipitation Over the Maritime Continent. Journal of Geophysical Research: Atmospheres, 124(2), 747–769. https://doi.org/10.1029/2018JD029474
Bett, P. E., Thornton, H. E., Troccoli, A., De Felice, M., Suckling, E., Dubus, L., Saint-Drenan, Y.-M., & Brayshaw, D. J. (2022). A simplified seasonal forecasting strategy, applied to wind and solar power in Europe. Climate Services, 27(August), 100318. https://doi.org/10.1016/j.cliser.2022.100318
Birch, C. E., Webster, S., Peatman, S. C., Parker, D. J., Matthews, A. J., Li, Y., & Hassim, M. E. E. (2016). Scale Interactions between the MJO and the Western Maritime Continent. Journal of Climate, 29(7), 2471–2492. https://doi.org/10.1175/JCLI-D-15-0557.1
Bloomfield, H. C., Brayshaw, D. J., Gonzalez, P. L. M., & Charlton-Perez, A. (2021). Sub-seasonal forecasts of demand and wind power and solar power generation for 28 European countries. Earth System Science Data, 13(5), 2259–2274. https://doi.org/10.5194/essd-13-2259-2021
Cai, W., Santoso, A., Wang, G., Yeh, S.-W., An, S.-I., Cobb, K. M., Collins, M., Guilyardi, E., Jin, F.-F., Kug, J.-S., Lengaigne, M., McPhaden, M. J., Takahashi, K., Timmermann, A., Vecchi, G., Watanabe, M., & Wu, L. (2015). ENSO and greenhouse warming. Nature Climate Change, 5(9), 849–859. https://doi.org/10.1038/nclimate2743
Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C., Hu, Z., & Qi, Y. (2024). A machine learning model that outperforms conventional global subseasonal forecast models. Nature Communications, 15(1), 6425. https://doi.org/10.1038/s41467-024-50714-1
Cruz, F. T., Narisma, G. T., Villafuerte, M. Q., Cheng Chua, K. U., & Olaguera, L. M. (2013). A climatological analysis of the southwest monsoon rainfall in the Philippines. Atmospheric Research, 122, 609–616. https://doi.org/10.1016/j.atmosres.2012.06.010
Delaunay, A., & Christensen, H. M. (2022). Interpretable Deep Learning for Probabilistic MJO Prediction. Geophysical Research Letters, 49(16), 1–10. https://doi.org/10.1029/2022GL098566
DeMott, C. A., Klingaman, N. P., & Woolnough, S. J. (2015). Atmosphere‐ocean coupled processes in the Madden‐Julian oscillation. Reviews of Geophysics, 53(4), 1099–1154. https://doi.org/10.1002/2014RG000478
Doblas‐Reyes, F. J., García‐Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. L. (2013). Seasonal climate predictability and forecasting: status and prospects. WIREs Climate Change, 4(4), 245–268. https://doi.org/10.1002/wcc.217
Fang, W., Yang, C., Liu, D., Huang, Q., Ming, B., Cheng, L., Wang, L., Feng, G., & Shang, J. (2023). Assessment of Wind and Solar Power Potential and Their Temporal Complementarity in China’s Northwestern Provinces: Insights from ERA5 Reanalysis. Energies, 16(20), 7109. https://doi.org/10.3390/en16207109
González-Aparicio, I., & Zucker, A. (2015). Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain. Applied Energy, 159, 334–349. https://doi.org/10.1016/j.apenergy.2015.08.104
Handayani, K., Krozer, Y., & Filatova, T. (2017). Trade-offs between electrification and climate change mitigation: An analysis of the Java-Bali power system in Indonesia. Applied Energy, 208(August), 1020–1037. https://doi.org/10.1016/j.apenergy.2017.09.048
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Huld, T., Müller, R., & Gambardella, A. (2012). A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy, 86(6), 1803–1815. https://doi.org/10.1016/j.solener.2012.03.006
Isa, M. A., Sudjono, P., Sato, T., Onda, N., Endo, I., Takada, A., Muntalif, B. S., & Ide, J. (2021). Assessing the Sustainable Development of Micro-Hydro Power Plants in an Isolated Traditional Village West Java, Indonesia. Energies, 14(20), 6456. https://doi.org/10.3390/en14206456
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., & Monge-Sanz, B. M. (2019). SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development, 12(3), 1087–1117. https://doi.org/10.5194/gmd-12-1087-2019
Kim, H., Ham, Y. G., Joo, Y. S., & Son, S. W. (2021). Deep learning for bias correction of MJO prediction. Nature Communications, 12(1), 3087. https://doi.org/10.1038/s41467-021-23406-3
Kim, H., Vitart, F., & Waliser, D. E. (2018). Prediction of the Madden–Julian Oscillation: A Review. Journal of Climate, 31(23), 9425–9443. https://doi.org/10.1175/JCLI-D-18-0210.1
Kurniadi, A., Weller, E., Min, S., & Seong, M. (2021). Independent ENSO and IOD impacts on rainfall extremes over Indonesia. International Journal of Climatology, 41(6), 3640–3656. https://doi.org/10.1002/joc.7040
Lim, Y., Son, S.-W., & Kim, D. (2018). MJO Prediction Skill of the Subseasonal-to-Seasonal Prediction Models. Journal of Climate, 31(10), 4075–4094. https://doi.org/10.1175/JCLI-D-17-0545.1
Lledó, L., & Doblas-Reyes, F. J. (2020). Predicting Daily Mean Wind Speed in Europe Weeks ahead from MJO Status. Monthly Weather Review, 148(8), 3413–3426. https://doi.org/10.1175/MWR-D-19-0328.1
Love, B. S., Matthews, A. J., & Lister, G. M. S. (2011). The diurnal cycle of precipitation over the Maritime Continent in a high‐resolution atmospheric model. Quarterly Journal of the Royal Meteorological Society, 137(657), 934–947. https://doi.org/10.1002/qj.809
Majidi Nezhad, M., Groppi, D., Marzialetti, P., Fusilli, L., Laneve, G., Cumo, F., & Garcia, D. A. (2019). Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands. Renewable and Sustainable Energy Reviews, 109(April), 499–513. https://doi.org/10.1016/j.rser.2019.04.059
Mariotti, A., Ruti, P. M., & Rixen, M. (2018). Progress in subseasonal to seasonal prediction through a joint weather and climate community effort. Npj Climate and Atmospheric Science, 1(1), 4. https://doi.org/10.1038/s41612-018-0014-z
Marshall, A. G., Hudson, D., Wheeler, M. C., Hendon, H. H., & Alves, O. (2012). Simulation and prediction of the Southern Annular Mode and its influence on Australian intra-seasonal climate in POAMA. Climate Dynamics, 38(11–12), 2483–2502. https://doi.org/10.1007/s00382-011-1140-z
Neale, R., & Slingo, J. (2003). The Maritime Continent and Its Role in the Global Climate: A GCM Study. Journal of Climate, 16(5), 834–848. https://doi.org/10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2
Osman, M., & Vera, C. S. (2017). Climate predictability and prediction skill on seasonal time scales over South America from CHFP models. Climate Dynamics, 49(7–8), 2365–2383. https://doi.org/10.1007/s00382-016-3444-5
Peatman, S. C., Matthews, A. J., & Stevens, D. P. (2014). Propagation of the Madden–Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation. Quarterly Journal of the Royal Meteorological Society, 140(680), 814–825. https://doi.org/10.1002/qj.2161
Rauniyar, S. P., & Walsh, K. J. E. (2011). Scale Interaction of the Diurnal Cycle of Rainfall over the Maritime Continent and Australia: Influence of the MJO. Journal of Climate, 24(2), 325–348. https://doi.org/10.1175/2010JCLI3673.1
Roberts, M. J., Vidale, P. L., Senior, C., Hewitt, H. T., Bates, C., Berthou, S., Chang, P., Christensen, H. M., Danilov, S., Demory, M.-E., Griffies, S. M., Haarsma, R., Jung, T., Martin, G., Minobe, S., Ringler, T., Satoh, M., Schiemann, R., Scoccimarro, E., … Wehner, M. F. (2018). The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale. Bulletin of the American Meteorological Society, 99(11), 2341–2359. https://doi.org/10.1175/BAMS-D-15-00320.1
Robertson, A. W., & Vitart, F. (2019). Subseasonal to seasonal prediction project: Bridging the gap between weather and climate. Elsevier. https://doi.org/10.1016/C2016-0-01594-2
Scheuerer, M., & Möller, D. (2015). Probabilistic wind speed forecasting on a grid based on ensemble model output statistics. The Annals of Applied Statistics, 9(3), 1328–1349. https://doi.org/10.1214/15-AOAS843
Schindler, D., Wehrle, J., Sander, L., Schlemper, C., Bekel, K., & Jung, C. (2025). Assessment of Spatiotemporal Wind Complementarity. Energies, 18(14), 3715. https://doi.org/10.3390/en18143715
Shin, N.-Y., Kim, D., Kang, D., Kim, H., & Kug, J.-S. (2024). Deep learning reveals moisture as the primary predictability source of MJO. Npj Climate and Atmospheric Science, 7(1), 11. https://doi.org/10.1038/s41612-023-00561-6
Sparks, A. (2018). nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R. Journal of Open Source Software, 3(30), 1035. https://doi.org/10.21105/joss.01035
Tangang, F. T., Juneng, L., Salimun, E., Sei, K. M., Le, L. J., & Muhamad, H. (2012). Climate change and variability over Malaysia: Gaps in science and research information. Sains Malaysiana, 41(11), 1355–1366.
van der Wiel, K., Bloomfield, H. C., Lee, R. W., Stoop, L. P., Blackport, R., Screen, J. A., & Selten, F. M. (2019). The influence of weather regimes on European renewable energy production and demand. Environmental Research Letters, 14(9), 094010. https://doi.org/10.1088/1748-9326/ab38d3
Vannitsem, S., Wilks, D. S., & Messner, J. W. (2018). Statistical Postprocessing of Ensemble Forecasts. Elsevier. https://doi.org/10.1016/C2016-0-03244-8
Vincent, C. L., & Lane, T. P. (2017). A 10-Year Austral Summer Climatology of Observed and Modeled Intraseasonal, Mesoscale, and Diurnal Variations over the Maritime Continent. Journal of Climate, 30(10), 3807–3828. https://doi.org/10.1175/JCLI-D-16-0688.1
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., … Zhang, L. (2017). The Subseasonal to Seasonal (S2S) Prediction Project Database. Bulletin of the American Meteorological Society, 98(1), 163–173. https://doi.org/10.1175/BAMS-D-16-0017.1
Wheeler, M. C., & Hendon, H. H. (2004). An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Monthly Weather Review, 132(8), 1917–1932. https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2
Wheeler, M. C., Hendon, H. H., Cleland, S., Meinke, H., & Donald, A. (2009). Impacts of the Madden–Julian Oscillation on Australian Rainfall and Circulation. Journal of Climate, 22(6), 1482–1498. https://doi.org/10.1175/2008JCLI2595.1
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J. T., Lazo, J. K., Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V., Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B., Graham, R., Kjellström, E., Becker, E., … Zebiak, S. E. (2017). Potential applications of subseasonal‐to‐seasonal (S2S) predictions. Meteorological Applications, 24(3), 315–325. https://doi.org/10.1002/met.1654
Yang, D., Kleissl, J., Gueymard, C. A., Pedro, H. T. C., & Coimbra, C. F. M. (2018). History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, 168(November 2017), 60–101. https://doi.org/10.1016/j.solener.2017.11.023
Zhang, C., Adames, Á. F., Khouider, B., Wang, B., & Yang, D. (2020). Four Theories of the Madden‐Julian Oscillation. Reviews of Geophysics, 58(3). https://doi.org/10.1029/2019RG000685
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Copyright (c) 2026 Nayla Shofwatun Nisa, Sahwahita Raisya Putri Ariwibowo, Nabila Nurkhalizah Ismail, Jogi Ruben Natanael Panggabean, Ranisaa Septiana, Kathryn Trijatha Putri Rahayu Jurika, Ritzky Malona, Nisrina Sahidatunisa (Author)
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