EnergyIQ: AI-Powered Intelligent Energy & Asset Management for Sustainable Smart Cities

The evolution of energy management in smart cities has shifted from static monitoring systems toward AI-driven, data-centric intelligence capable of forecasting, diagnosis, and operational optimization. Modern SCADA and Building Management Systems (BMS) generate high-frequency data from cooling plants, chillers, pumps, and other critical infrastructure assets; however, these systems remain largely reactive, siloed, and underutilized for advanced analytics. As a result, significant opportunities exist to transform operational data into actionable intelligence for energy optimization, demand-side management (DSM), and predictive asset management. Recent advances in artificial intelligence—particularly deep neural networks, transformer-based forecasting models, physics-informed neural networks (PINNs), and federated learning—have demonstrated strong capabilities in fault detection, degradation modeling, and short-term load prediction. PINNs embed thermodynamic and electromechanical constraints directly into neural architectures, enabling physically consistent modeling of complex energy systems such as HVAC plants and cooling networks. Federated learning further enables collaborative model training across distributed infrastructure while preserving data privacy, an essential requirement for multi-facility energy systems. AI-enabled energy management applications are increasingly being developed to support intelligent load forecasting, predictive maintenance, anomaly detection, and operational optimization of large-scale cooling and energy infrastructure. These technologies can significantly improve system efficiency, reliability, and resilience while reducing operational costs and carbon emissions. However, most existing solutions remain fragmented, focusing either on asset monitoring or energy analytics without integrating both within a unified operational framework. Consequently, a comprehensive AI-enabled architecture that combines energy management, predictive maintenance, demand-side management, and sustainability analytics within a privacy-preserving digital infrastructure remains largely underdeveloped—particularly in cooling-dominant regions such as the GCC.

Hamad Bin Khalifa University (HBKU)