A Rule-based Control Approach Using Neural Network and Optimized Battery Energy Management to Efficiently Integrate Renewable Energy Sources in Data Center

The rapid growth of data center energy demand, combined with the urgency to decrease carbon footprint, has led to the development of new energy management systems. The thesis aims to optimize energy management in data centers using a rule-based control framework which con-siders renewables, battery storage and local grid power. The proposed framework also uses a Neural Network forecasting model capable of predicting solar PV generation and integrates two distributed rule-based controllers for dynamic energy sourcing management. The grid-side controller maintains power usage from the grid according to dynamic electricity prices and PV forecast values. The controller on the battery-side manages and optimizes the operation of a battery storage system based on their state-of-charge. The proposed system was simulated using MATLAB R2024B software and the control logic is simplified and verified through various scenario-based analysis and Karnaugh mapping. The outcomes satisfy the capabilities of the proposed framework in terms effectively prioritize renewable energy usage, minimize grid power dependency and ensure secure operations under different conditions based on – PV generation, load demand, peak hours and emergency black-out scenarios. The framework combines hybrid energy sources and battery storage using rule-based control, which aligns the sustainable development goals for carbon emission reduction and acquisition of green energy practices in the domain of data center. In future work this study suggests a real-world implementation and the inclusion of advanced control strategies like fuzzy logic, reinforcement learning, model predictive control to better control the system. This re-search provides a scalable and cost-effective solution for optimizing energy management in data centers for ensuring a sustainable energy future.