AI-Enabled Energy Management of Battery Storage Systems for Data Centres and Industrial Facilities in Saudi Arabia

Saudi Arabia's data centres and industrial facilities are entering a phase where electrical reliability, decarbonisation, and digital operational intelligence must be addressed as an integrated design challenge. Battery energy storage systems (BESS) facilitate this transition by mitigating renewable variability, reducing peak demand, partially replacing diesel backup, and providing rapid power support during grid disturbances. However, batteries introduce constraints such as ageing, thermal stress, safety limitations, cyber risks, and potential conflicts between reliability and economic objectives. This review develops an AI-enabled energy management framework for BESS deployment in Saudi data centres and industrial facilities. It synthesises literature from 2020 to 2025 on battery management systems, hybrid microgrids, renewable forecasting, cloud-based battery intelligence, data-centre energy flexibility, and AI risk governance. The objective is to demonstrate how machine learning, model predictive control, reinforcement learning, digital twins, and anomaly detection can be integrated into a secure energy management system. The paper proposes a layered architecture that translates load telemetry, weather forecasts, grid signals, battery state estimation, and facility resilience policies into auditable control actions. The findings indicate that AI-enabled energy management systems (EMS) can enhance peak-load management, renewable utilisation, battery health, outage preparedness, and emissions performance, provided that models are governed by safety protocols, human oversight, and cybersecurity measures. The review concludes with a Saudi-specific research agenda focused on pilot projects, interoperability standards, and operational governance.