In the pursuit of sustainable industrial operations, efficient energy management has become a critical challenge, particularly under scenarios where the electrical grid is restricted to serving industrial loads. This study addresses the urgent need for intelligent forecasting and scheduling frameworks by proposing a hybrid Gene Expression Programming Adaptive Neuro-Fuzzy Inference System (GEP-ANFIS) for predictive energy management in hybrid renewable energy systems. The model was evaluated using standard forecasting metrics. For solar PV prediction, GEP-ANFIS achieved low short- and long-term error rates, with MAPE values below 6% and 8%, respectively. For industrial load forecasting, the model exhibited high precision, maintaining MAPE values under 2.5% (short-term) and under 3.5% (long-term). These results demonstrate consistent improvements over conventional ANFIS and GEP models. Economic evaluation confirmed significant cost benefits. In a Grid-only configuration, GEP-ANFIS reduced daily energy costs by 7.4% compared to ANFIS. Greater efficiency was observed in PV and Battery-only and Grid-connected PV-Battery setups, where GEP-ANFIS achieved daily cost reductions of 6.5% and 6.3%, respectively. Over a 20-year planning horizon, the system recorded a 6.5% reduction over ANFIS and a 37.7% improvement over HOMER. A sensitivity analysis was also conducted to assess the robustness of the GEP-ANFIS model under varying solar PV power, and battery storage capacity. Results indicated the robustness, efficiency, and scalability of the GEP-ANFIS controller, especially in resource-constrained, PV-dominated microgrids, making it a strategic solution for sustainable industrial energy management while preserving battery longevity by avoiding deep discharge scenarios.

<p>The fast development of worldwide energy consumption, driven by industrial growth and increasing dependence on fossil fuels, has led to higher carbon emissions and degradation of the environment. In response, renewable energy sources, such as solar, wind, and hydroelectric power, offer cleaner and sustainable replacements with insignificant carbon emissions. This paper examines the role of artificial intelligence (AI)-based techniques, particularly fuzzy logic, in developing energy management system. A fuzzy logic-based energy management system is proposed for a renewable-powered microgrid that incorporates a hybrid energy storage system. Fuzzy logic-based energy management, due to its capability to manage uncertainty and complexity, offers viable solutions for improving the generation and distribution of energy within microgrid systems. This system is compared to a dynamic cascaded dual-loop proportional-integral (PI) controller-based energy management system in standalone mode. The comparative analysis emphasizes the ability of fuzzy logic-based energy management to improve the efficiency and sustainability of microgrids. The research aims to advance the creation of more intelligent and dependable energy solutions that integrate renewable resources and enhance energy management practices.</p>

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