AI-Driven Smart Energy Scheduling for Telecom Microgrids using Demand Forecasting and Outage Prediction

This paper presented an AI-driven smart energy scheduling system for telecom microgrids by integrating demand forecasting and outage prediction into a unified decision-making framework. The proposed approach combines an LSTM-based model for short-term energy demand prediction with a Random Forest classifier for estimating outage probability, enabling the system to make proactive and context-aware scheduling decisions. The results demonstrate that the predictive scheduler achieves consistent reductions in operational cost and diesel consumption compared to conventional reactive methods. Although the improvements are moderate, they highlight the effectiveness of incorporating predictive intelligence into energy management systems. The inclusion of necessity-aware logic further enhances system efficiency by avoiding unnecessary battery charging and ensuring optimal resource utilization. Overall, the proposed system improves energy efficiency while maintaining reliability under uncertain conditions, making it a suitable approach for real-world telecom applications. Future work may focus on improving outage prediction accuracy, incorporating real-time data, and extending the system to multi-tower environments for large-scale deployment.  

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