Artificial Intelligence-Based Intelligent Energy Management for Sustainable Reduction in Electricity Usage
Year: 2026
Type: article
Abstract: 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.
Institution: St. Francis Institute of Technology
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124417226
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