These pressures have been heightened by the increasing volatility in power markets and growing demand for electricity globally. This work presents an AI-powered energy management framework that consists of three major components: (i) LSTM networks for accurate demand forecasting, (ii) Random Forest classifiers for robust anomaly detection, and (iii) a reinforcement learning-based scheduling algorithm for dynamic load optimization. Unlike existing works, heavily relying on IoT-integrated infrastructures, the proposed system performs effectively with legacy metering data, thus enhancing scalability while reducing deployment costs. Experiments on real-world consumption datasets demonstrate key performance gains: peak load reduction by 18%, savings in operational costs by 14%, overall energy efficiency improvement by 21%, and 96% anomaly detection accuracy. The obtained results confirm the validity of integrating forecasting, anomaly detection, and intelligent scheduling in one unified data-centric framework. The proposed solution offers an efficient, adaptive approach that is environmentally friendly for optimizing electricity usage in residential, commercial, and industrial settings.
Tarade et al. (2026) studied this question.