National Electricity Demand Forecasting using Hybrid Statistical-Scenario and Monte Carlo Simulation - Dira Dini Dian Kemala (Statistika, 2026)
Abstract. National electricity demand is a key strategic indicator for planning and managing of Indonesia’s electricity system. Economic growth, demographic dynamics, and energy policies drive a steady increase in electricity demand, creating uncertainty in forecasting electricity demand. This study aims to develop an electricity demand forecasting model based on a hybrid statistical approach that probabilistically models uncertainty. The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model is used to capture the influence of exogenous variables, then combined with Monte Carlo simulations to produce scenario-based projections. Secondary data from BPS, PLN, ESDM, and the Ministry of Trade, covering for the period 2003–2024 are used in the analysis. Based on evaluations of several performance metrics, the ARIMAX (1,1,1) model was selected as the best. The electrification ratio variable is modeled using a beta distribution, while the GDP growth, population, and electricity tariff variables are modeled using lognormal distributions. The projection results for the period 2025–2035 show that all scenarios produce electricity consumption ranging from 381 to 430 terawatt-hours (TWh) in 2035. Monte Carlo simulations suggest that the electrification ratio primarily affects projection uncertainty, while economic and demographic variables remain relatively stable. This approach provides more comprehensive probabilistic range information than a single deterministic estimate, thereby supporting more adaptive, risk-based energy planning.