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A Multimodal Dataset for Large Language Model Applications in the Energy Domain

arXiv 2607.11459 ↗First posted 13 Jul 2026eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector. The dataset integrates approximately 50,000 textual documents, 20,000 images, 25 million numerical time series records, and 2 million geospatial and relational data entries. It includes policy and regulatory texts, scientific articles and news articles, satellite and contextual imagery, electricity system measurements, weather observations, statistical indicators, and geospatial representations of energy infrastructure and related entities. All data have been harmonized into structured, ready-to-use formats, accompanied by consistent metadata and reproducible data retrieval and preparation workflows. The dataset can serve as a foundational energy knowledge base, allowing energy stakeholders to integrate additional open-source or proprietary data. The mAIEnergy dataset adheres to Findable, Accessible, Interoperable, and Reusable (FAIR) principles, enhancing its applicability for AI-driven energy research, modeling, and decision-making.

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References · 13

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  11. 2024An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictionscited by 11
  12. 2025Towards EnergyGPT: A Large Language Model Specialized for the Energy Sectorcited by 3
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  1. 2025From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twinscited by 2

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