Intelligent Energy Management: A Comprehensive Review of

Artificial Intelligence, Machine Learning, and Metaheuristic

Optimization Approaches

Faustino D. Reyes1,*

1 ICT Bahrain Polytechnic,PO Box 33349, Isa Town, Bahrain

Received: May 18, 2026 Revised: July 01, 2026 Accepted: August 29, 2026 ⋆ Corresponding author

ABSTRACT

Intelligent energy management is increasingly important for improving energy efficiency, reliability, economic

performance, and sustainability across buildings, smart homes, industrial facilities, microgrids, and interconnected

energy systems. This review examines recent advances integrating machine learning, deep learning, reinforcement

learning, Internet of Things technologies, predictive analytics, feature selection, and metaheuristic optimization.

These methods support energy-consumption forecasting, demand response, renewable-energy coordination, load

scheduling, anomaly detection, building optimization, smart-home control, industrial energy management, and

microgrid operation. The reviewed studies indicate that intelligent approaches can improve forecasting accuracy,

reduce energy costs, optimize resource allocation, and enhance real-time decision-making under uncertain conditions.

Metaheuristic algorithms further improve performance through parameter optimization, feature selection, and multiobjective

scheduling. However, practical deployment remains challenged by data quality, computational complexity,

cybersecurity, privacy, interoperability, generalizability, transparency, infrastructure cost, and communication

reliability. Future research should prioritize explainable, scalable, secure, and adaptive intelligent energy-management

frameworks for efficient, resilient, flexible, and low-carbon energy system operation.

Keywords: Agentic artificial intelligence Autonomous agents Large language models Multi-agent systems

Human-centered governance

1. INTRODUCTION

The continuing transformation of modern energy systems has

substantially increased the complexity associated with the production,

distribution, consumption, monitoring, and coordination

of electrical energy. Conventional energy infrastructures

were predominantly designed around centralized generation,

comparatively predictable consumption profiles, and operational

strategies governed by predefined rules and deterministic

control procedures. Contemporary energy environments

differ considerably from this traditional configuration. Increasing

electricity demand, the widespread deployment of

distributed resources, the growing penetration of renewable

generation, the electrification of transportation and industrial

activities, and the continuous expansion of digitally connected

devices have created energy systems characterized by greater

variability, decentralization, and operational uncertainty. At

the same time, modern energy infrastructures generate extensive

streams of heterogeneous information through smart

meters, sensing devices, communication networks, automated

controllers, and digitally monitored equipment. The resulting

environment creates an important requirement for manage-