Strategies for Energy Management in Smart Grids Using Artificial Intelligence and Predictive Analytics

The increasing deployment of renewable energy sources, decentralized generation, EV, and dynamic consumers has made modern smart grids complex cyber-physical systems, making conventional energy management methodologies ineffective for real-time operations and decision-making. Currently used optimization algorithms work within closed and restricted environments, possess weak flexibility to uncertain conditions, and lack autonomy and predictive abilities for multi-layer operation. Therefore, the present study suggests a sophisticated Artificial Intelligence and Predictive Analytics-based Energy Management Model that performs continual forecast of grid demand, renewable generation, storage behavior, and network restrictions while executing adaptive optimization. The new method uses innovative Multi-Layer Cognitive Energy Twin, which builds a constantly evolving virtual representation of grid behavior; Adaptive Predictive Resilience Index for early prediction of stability, and Self-Evolving Collaborative Intelligence Engine which dynamically modifies operational strategies without following specific regulations. Also, the new system will use a Cross-Domain Energy Knowledge Fusion technique, which will learn from consumer preferences, environment, markets, and infrastructure, and will generate optimal strategies of energy management. Instead of reacting to unpredictable events, the proposed solution predicts grid states in the future and autonomously chooses energy management strategies, which increase efficiency, sustainability, resilience, and economy.

Artificial Intelligence, Predictive Analytics, Energy Management for the Smart Grid, Cognitive Energy Optimization, Intelligent Control of the Grid.

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