Novel Integration of Ant Colony Optimisation and Deep Neural Networks in AI Agents for Predictive Maintenance of Sustainable Energy Systems

Document Type

Article

Source of Publication

World Journal of Science Technology and Sustainable Development

Publication Date

6-1-2026

Abstract

PURPOSE: This study investigates how renewable energy firms utilise Artificial Intelligence (AI)-powered solutions to balance ecological, economic, and operational value in sustainable infrastructure. DESIGN/METHODOLOGY/APPROACH: A hybrid framework was developed, combining Deep Neural Networks (DNNs) and Ant Colony Optimisation (ACO) to train autonomous AI agents. The model was validated using multimodal sensor data from wind turbines, photovoltaic panels, and smart grids. FINDINGS: The framework significantly improved fault detection and maintenance optimisation. Organisationally, swarm intelligence enabled cost-effective resource allocation. Environmentally, the system reduced energy waste and carbon emissions while increasing grid reliability. ORIGINALITY/VALUE: This research uniquely integrates swarm intelligence with deep learning, reconceptualising AI as a foundational agent for autonomous energy management and sustainability. PRACTICAL IMPLICATIONS: The framework provides a roadmap for energy executives to optimise maintenance, achieve decarbonisation goals, and ensure stability through AI-driven resource management.

ISSN

2042-5945

Publisher

World Association for Sustainable Development (WASD)

Volume

21

Issue

4

First Page

321

Last Page

342

Disciplines

Computer Sciences

Keywords

AI Agent, Ant Colony Optimisation, Deep Neural Networks, Predictive Maintenance, Smart Grids, Sustainable Energy, Swarm Intelligence

Scopus ID

105043518117

Indexed in Scopus

yes

Open Access

no

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