Hybrid AI Models for Optimizing Solar–Wind Hybrid Microgrids in Smart Cities

Abstract

This paper presents a comprehensive investigation into the design and implementation of hybrid artificial intelligence (AI) models for the operational optimization of solar–wind hybrid microgrids within smart city environments. The proposed hybrid AI framework integrates machine learning techniques—including deep learning, reinforcement learning, and evolutionary algorithms—to enable accurate forecasting of renewable generation, dynamic energy management, and adaptive control under variable weather and load conditions. The methodology encompasses data acquisition from weather stations, load profiles, and distributed energy resources; model training, validation, and hybrid ensemble construction; and multi-objective optimization focusing on minimizing energy cost, maximizing reliability, and reducing carbon footprint. Simulation results utilizing realistic smart-city datasets demonstrate that the hybrid AI approach significantly outperforms baseline models in terms of prediction accuracy (reducing mean absolute error by up to 25 %) and operational efficiency (lowering cost by 15 % and emissions by 20 %) in comparison to conventional single-technique methods. The findings underscore the potential of hybrid AI-driven control strategies to elevate the resilience and sustainability of solar–wind microgrids, thereby contributing to the advancement of intelligent energy systems in urban contexts.