MULTI-OBJECTIVE NEURO-FUZZY ENERGY MANAGEMENT CONTROLLER FOR SUSTAINABLE SMART CITY SYSTEMS

The rapid urbanization that has occurred across the globe has intensified the demand for energy-efficient infrastructures in the smart city domain. Conventional control strategies have struggled to address the dynamic, nonlinear, and uncertain nature of urban energy systems. Intelligent controllers that have combined learning capability with human-like reasoning have therefore attracted increasing attention for sustainable city development. Despite notable progress, many existing energy management frameworks have remained limited by single- objective optimization and rigid control logic. These limitations have reduced adaptability under fluctuating demand, heterogeneous data streams, and conflicting performance goals such as energy efficiency, stability, and operational cost. An effective solution has required a controller that has balanced multiple objectives while maintaining interpretability and robustness. This study has proposed a multi- objective neuro-fuzzy controller that has integrated fuzzy inference with neural learning for smart city energy management. The controller architecture has incorporated adaptive membership functions and rule bases that have evolved through multi-objective optimization. Energy consumption, system stability, and response efficiency have been jointly optimized using a Pareto-based learning mechanism that has guided parameter tuning. The simulation framework has modeled urban energy scenarios that have included variable loads, renewable integration, and stochastic demand patterns. The experimental evaluation demonstrates that the proposed controller achieves significant improvements over conventional methods. Energy consumption reduces to 360 kWh compared with 430 kWh (FLC), 410 kWh (ANN), and 395 kWh (SONFC). Stability index increases to 0.93, and response time decreases to 1.2 s. Energy savings reach 21.9% at peak loads, while control efficiency improves to 93%, confirming the controller’s adaptability and superior performance under dynamic urban energy scenarios.