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<p>Forecasting renewable energy development in India requires methods that represent both predictable growth in generation capacity and the uncertainty driven by climate variability, technology costs, and policy decisions. This study proposes an integrated stochastic and data-driven framework in which solar capacity, wind capacity, electricity storage, and national electricity demand are modeled as continuous-time stochastic processes. A quadratic economic performance measure links random fluctuations in these processes to long-term investment expenditure and the reliability of electricity supply. The stochastic system is solved numerically using Monte Carlo simulation and combined with a sequence-based neural network and a gradient boosting regression method to refine trend prediction. Model parameters are calibrated using nationally reported energy and meteorological data for the period 2010–2024 published by Indian government agencies. The empirical results show that the framework attains high forecasting accuracy and produces probability ranges for future capacity trajectories that remain consistent with observed historical volatility. Scenario analysis indicates that lower uncertainty in renewable generation reduces optimal storage requirements and can yield substantial savings in overall system cost while maintaining reliability of supply. Overall, the proposed framework provides a transparent and flexible tool for designing renewable expansion pathways that remain robust to uncertainty and aligned with India’s long-term climate and energy security objectives, including the national Net-Zero&#xa0;2047 roadmap.</p>

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A Hybrid Stochastic Differential Equation–Machine Learning Framework for Uncertainty-Aware Renewable Energy Forecasting and Policy Optimization in India

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Sumit Saha,
  • Sourav Nath

摘要

Forecasting renewable energy development in India requires methods that represent both predictable growth in generation capacity and the uncertainty driven by climate variability, technology costs, and policy decisions. This study proposes an integrated stochastic and data-driven framework in which solar capacity, wind capacity, electricity storage, and national electricity demand are modeled as continuous-time stochastic processes. A quadratic economic performance measure links random fluctuations in these processes to long-term investment expenditure and the reliability of electricity supply. The stochastic system is solved numerically using Monte Carlo simulation and combined with a sequence-based neural network and a gradient boosting regression method to refine trend prediction. Model parameters are calibrated using nationally reported energy and meteorological data for the period 2010–2024 published by Indian government agencies. The empirical results show that the framework attains high forecasting accuracy and produces probability ranges for future capacity trajectories that remain consistent with observed historical volatility. Scenario analysis indicates that lower uncertainty in renewable generation reduces optimal storage requirements and can yield substantial savings in overall system cost while maintaining reliability of supply. Overall, the proposed framework provides a transparent and flexible tool for designing renewable expansion pathways that remain robust to uncertainty and aligned with India’s long-term climate and energy security objectives, including the national Net-Zero 2047 roadmap.