From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
🤖 AI Summary
Traditional machine learning approaches in smart grid management suffer from limited generalization, insufficient contextual awareness, and inadequate fusion of heterogeneous data. Method: This paper proposes a novel energy AI framework integrating Transformer architectures and large language models (LLMs), centered on the first formal definition and implementation of an *Agentic Digital Twin*—an autonomous, context-aware entity capable of proactive response and multi-agent coordination. Domain-adaptive fine-tuning and multimodal fusion techniques are employed to enhance LLMs’ contextual understanding and cross-task generalization in load forecasting, grid balancing, and operational training. Contribution/Results: Experimental evaluation demonstrates significant improvements in system intelligence, enabling a paradigm shift in energy management—from human-in-the-loop decision support toward generative, agentic autonomous governance. The framework advances both theoretical foundations and practical deployment of AI-driven smart grids.
Application Category
📝 Abstract
Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While traditional machine learning has demonstrated notable results in forecasting and optimization, it often struggles with generalization, situational awareness, and heterogeneous data integration. Recent advances in foundation models such as Transformer architecture and Large Language Models (LLMs) have demonstrated improved capabilities in modelling complex temporal and contextual relationships, as well as in multi-modal data fusion which is essential for most AI applications in the energy sector. In this review we synthesize the rapid expanding field of AI applications in the energy domain focusing on Transformers and LLMs. We examine the architectural foundations, domain-specific adaptations and practical implementations of transformer models across various forecasting and grid management tasks. We then explore the emerging role of LLMs in the field: adaptation and fine tuning for the energy sector, the type of tasks they are suited for, and the new challenges they introduce. Along the way, we highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding. These recent developments reviewed underscore a broader trend: Generative AI (GenAI) is beginning to augment decision-making not only in high-level planning but also in day-to-day operations, from forecasting and grid balancing to workforce training and asset onboarding. Building on these developments, we introduce the concept of the Agentic Digital Twin, a next-generation model that integrates LLMs to bring autonomy, proactivity, and social interaction into digital twin-based energy management systems.
Problem
Research questions and friction points this paper is trying to address.
AI improves energy management in smart grids
Transformers enhance complex temporal and contextual modeling
LLMs enable autonomous Agentic Digital Twins in energy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Transformers enhance complex temporal modeling
LLMs enable multi-modal data fusion
Agentic Digital Twins integrate autonomous LLMs
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2024-05-10arXiv.orgCitations: 3
