Enhancing Climate Adaptive Energy Management Through Intelligent Control Mechanisms
Document Type : Original Research Article
Received: 18 June 2026
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Revised: 08 July 2026
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Accepted: 20 July 2026
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Published: 28 July 2026
Authors
JSEEGT Author 6
1,*
1
Independent Researcher
*
Author to whom correspondence should be addressed.
Abstract
The increasing adoption of renewable energy sources, electric vehicles and smart grid systems has introduced significant challenges in energy forecasting, grid regulation and real time power management due to renewable intermittency, climate variability and fluctuating energy demand. This research proposes Variability Energy Regulation Grid Equilibrium (VERGE), an integrated model comprising Climate Mamba Former, Dynamic Spatio-Temporal Graph Transformer (DSTGT) and Safe Reinforcement Learning-Model Predictive Control (Safe RL-MPC). The Climate Mamba Former (CMF) forecasts electricity demand and renewable energy generation by incorporating weather conditions, while the DSTGT models interactions among solar systems, wind systems, batteries, electric vehicle chargers, loads and grid components. The Safe RL-MPC controller determines optimal energy regulation actions while maintaining operational safety constraints. Experimental results demonstrate that VERGE achieves a forecasting accuracy of 99.1% and frequency stability of 98.9%, outperforming existing state-of-the-art energy management approaches. Statistical validation using p-values, confidence intervals and ANOVA further supports the reliability of the obtained results. Overall, VERGE provides an effective approach for intelligent energy forecasting, renewable energy management, power imbalance reduction and improved grid stability under dynamic operating conditions.
Graphical Abstract
Keywords:
Adaptive Energy Management, Climate-Aware Forecasting, Dynamic Graph Learning, Grid Stability, Renewable Energy Integration, Safe Reinforcement Learning
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