Intelligent energy management systems for buildings in smart cities using PSO-driven optimization and penalty function constraints

Abstract

The growing need for energy conservation and sustainable development in smart urban areas demands the implementation of sophisticated Energy Management Systems (EMS) in modern buildings. This study presents a design for an intelligent EMS that merges Particle Swarm Optimization (PSO) with constraint-driven penalty functions to dynamically optimize energy consumption while maintaining both operational effectiveness and occupant comfort. The system efficiently oversees diverse energy consuming subsystems, including lighting, HVAC, and ventilation, by utilizing real-time data from environmental conditions and human presence, using a distributed wireless sensor network. The hardware setup features smart sensors and controllable nodes that monitor occupancy, temperature, ambient light, and air quality, enabling the coordinated management of energy-demanding systems. Central to the software architecture is a PSO-based control algorithm, augmented with penalty functions to impose crucial constraints, such as thermal comfort, minimum light levels, and ventilation standards. This hybrid optimization structure ensures that every control decision supports both energy conservation goals and human-centered needs. The novelty of the proposed EMS lies in its low-cost deployment, simplified integration with existing infrastructures, and enhanced interoperability across building systems, addressing key limitations of current solutions such as high complexity, inflexible architectures, and poor scalability. Experimental assessments in a simulated smart building setting reveal the system’s adaptability, reliability, and prompt responses in real-time. The EMS successfully detected varying occupancy and environmental patterns, finely tuning energy allocation across subsystems. Numerical analyses indicate up to 30% overall energy savings, confirming the efficacy of the PSO-penalty optimization method. The proposed EMS provides a scalable, interoperable, and cost-effective solution, making it apt for integration into current infrastructures and supportive of future-ready smart city environments.