Uncertain processes in industrial energy systems: Modeling and Prognosis

The increasing variability of industrial energy systems requires advanced methods for modeling and forecasting uncertain processes, such as generation fluctuations, load variations, and the influence of meteorological factors. This work proposes an integrated approach based on probabilistic models, machine learning techniques, and digital twin simulation, implemented within a distributed, fault-tolerant architecture. The simulation results demonstrate that the Digital Twin framework significantly enhances forecast accuracy and reduces energy imbalance compared to traditional methods. By calculating the mean absolute errors across multiple simulation runs, the model quantifies the exact improvements in forecasting precision and systemic imbalance mitigation. Integration with distributed orchestration mechanisms enables adaptive management of computational resources in the presence of faults. The proposed approach therefore demonstrates significant potential for increasing the reliability, efficiency, and resilience of industrial energy systems. The scientific novelty of this work lies in the synergistic integration of three key components: probabilistic uncertainty modeling, data-driven adaptive learning, and implementation in an orchestrated distributed infrastructure. Unlike existing approaches, which often address these aspects separately, this study proposes a unified framework in which predictions directly influence decision-making processes and the management of computational resources.