Uncertainty Analysis at Poigar River Discharge Base On El Nino Southern Ocillation (ENSO) Index Impact Using Monte Carlo Method
Keywords:
hydro power plant, ENSO, efficiency, machine learning, NINO 3.4Abstract
PLN has established long-term objectives detailed in Electricity Supply Business Plan for 2021-2030, focusing providing an adequate, reliable, and efficient electricity supply to ensure national energy security such like Hydro Power Plant Development, leveraging Indonesia abundant river system as sustainable energy sources. However, this development is highly sensitive to climate and local hydrological condition. One major external factor is El Niño Southern Oscillation (ENSO). The Sea Surface Temperature NINO represents ENSO data, which serves as an external indicator reflecting changes in sea surface temperature, which are used to identify the occurrence of La Niña or El Niño events. These fluctuations greatly affect river discharge and affecting in electricity generation by Hydropower Plants. This study proposes a machine learning based model that integrates SST NINO 3.4 as dynamic indicators with local climate and hydrological data to improve dependable flow estimation. The SEMMA framework was used for structured analysis, from data exploration to model assessment, applying both Monte Carlo and Latin Hypercube sampling methods. Using SEMMA framework was used for structured analysis, from data exploration to model assessment, applying both Monte Carlo and Latin Hypercube sampling methods. Results show that using SST NINO 3.4 Index help improves model performance across all metrics.
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