A Meta-Pipeline for Artificial Intelligence-Driven Homeostatic Control and Distributed Resource Optimization in Sustainable Energy Systems

The transition toward sustainable energy systems is increasing the operational complexity of modern power grids due to the high penetration of renewable energy sources, distributed energy resources, and bidirectional energy flows. Artificial intelligence has emerged as a key enabling technology for forecasting, optimization, and control in smart grids. Current AI implementations in energy systems lack unified workflows integrating forecasting, decision-making, adaptive stability regulation, and distributed coordination. Moreover, existing control approaches rarely incorporate biologically inspired stability mechanisms such as homeostatic regulation, limiting system-level resilience under dynamic operating conditions. This work aims to develop an architectural framework in the form of a unified artificial intelligence meta-pipeline enabling homeostatic control and distributed resource optimization in sustainable energy systems through closed-loop intelligent operation. A layered artificial intelligence meta-pipeline architecture is proposed integrating system representation, data intelligence, decision intelligence, homeostatic feedback regulation, and distributed coordination. A formal Homeostatic Energy Index is introduced to quantify system stress and enable supervisory adaptive policy regulation. The framework is validated using a reproducible microgrid-level simulation combining reinforcement learning-based control with homeostatic feedback regulation. Experimental validation demonstrates stable closed-loop operation under stochastic demand and renewable variability. The framework maintains bounded system stress levels, achieving an average Homeostatic Energy Index of 18.17 while preserving near-zero energy imbalance performance, confirming that homeostatic feedback improves stability without degrading energy balancing performance. This work introduces a unified artificial intelligence meta-pipeline architectural framework and formally defines a homeostatic feedback layer for sustainable energy system control. The proposed approach enables stability-aware structured integration of heterogeneous AI components and provides a foundation for self-adaptive, resilient, and distributed intelligent energy systems.