Data‐Driven Energy Planning in Developing Countries: An Integrated LEAP‐Machine Learning Approach

Developing countries encounter substantial obstacles in regional energy planning stemming from scarce historical data and insufficient forecasting methodologies, thereby impeding the formulation of effective sustainable energy transition strategies. Conventional Long‐range Energy Alternative Planning (LEAP) models frequently depend on static assumptions that diminish projection accuracy, especially under data‐limited conditions. This study develops and applies an integrated hybrid algorithm–LEAP (HA–LEAP) framework to improve the precision of long‐term regional energy planning and assess sustainable energy transition scenarios in Gorontalo Province, Indonesia. The research utilizes a quantitative case study methodology that integrates artificial intelligence with LEAP modeling, incorporating genetic algorithms and various optimization techniques. The HA–LEAP framework exhibits significant predictive performance enhancements compared to conventional LEAP approaches, achieving higher accuracy and diminished forecasting errors. Modeling results project a fundamental transformation in Gorontalo’s energy portfolio, with renewable energy penetration rising from 1.0% in 2015–41.8% by 2050, while petroleum dependency declines by 83% to 15.4%. Energy elasticity projections indicate successful decoupling between economic growth and energy consumption. This investigation presents the first comprehensive artificial intelligence‐integrated energy planning methodology specifically designed for developing regions experiencing data constraints. The framework provides policymakers with superior analytical instruments for evidence‐based sustainable energy transition planning, demonstrating the viability of ambitious renewable energy targets while preserving energy security objectives.