Dynamic Energy Demand Forecasting and Economic Optimization of Nepalese Industries through AI-Assisted Differential Equation Modeling
Purpose: This study develops a methodological framework integrating artificial intelligence (AI) and differential-equation models for dynamic industrial energy-demand forecasting and economic optimization in Nepal, considering production schedules, electricity conditions, tariffs, equipment characteristics, and data limitations.
Research Methodology: The framework models production and equipment dynamics using state-space ordinary differential equations, employs AI to predict short-term demand disturbances and residuals, and applies numerical integration, including the fourth-order Runge–Kutta (RK4) method, to simulate operational trajectories. Constrained receding-horizon optimization minimizes energy costs while satisfying production requirements and operational limits. Laplace-domain analysis supports interpretation of linearized system dynamics. Nepalese institutional energy sources provide contextual grounding.
Results: The framework demonstrates that industrial AI performance requires evaluation beyond forecasting accuracy, incorporating energy costs, production-normalized consumption, constraint violations, uncertainty calibration, and operational reliability. Synthetic demonstrations illustrate load profiles, forecasting comparisons, sensitivity assessments, and uncertainty analyses.
Conclusions: A staged validation approach is proposed, emphasizing chronological testing, transparent benchmarks, shadow-mode evaluation, operator supervision, and independent safety safeguards.
Limitations: The absence of plant-level operational data restricts the analysis to synthetic demonstrations, preventing empirical verification of energy savings, forecasting accuracy, or causal relationships.
Contributions: This study provides a reusable methodological framework integrating AI forecasting, dynamic system modeling, uncertainty assessment, and constrained economic optimization, establishing a foundation for future empirical applications in Nepalese industries.
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