Machine-Learning-Driven Predictive Control for Smart Energy and Industrial Systems

Abstract

Smart energy systems rely on accurate forecasting, adaptive control, and efficient resource allocation to maintain stable and sustainable operations. Traditional model-based control strategies face challenges due to the nonlinear, multivariate, and stochastic nature of modern energy demand and distributed generation. This paper introduces a machine-learning-driven predictive control methodology employing long short-term memory (LSTM) networks, random forests, and hybrid optimization algorithms to anticipate system behavior and generate optimal control actions. A feedback-enhanced predictive layer continuously retrains itself using real-time operational data, ensuring accuracy even under volatile conditions. The system is tested on microgrids, HVAC automation, and industrial thermal plants, demonstrating substantial reductions in energy waste, peak demand, and operational cost. The results highlight the capability of ML-enhanced control mechanisms to operate as intelligent coordinators across multiple energy-intensive domains.

KEYWORDS: Predictive Control, Machine Learning, Smart Energy Systems, Optimization, LSTM Networks