arXiv:2607. 11459v1 Announce Type: cross Abstract: This paper presents the mAIEnergy dataset, an open-access, multimodal corpus developed to support Large Language Model (LLM) applications in the energy sector.
arXiv:2607. 10720v1 Announce Type: new Abstract: The accelerating shift toward low-carbon power systems, together with the widespread adoption of behind-the-meter technologies such as rooftop solar and electric vehicles, is placing new operational and analytical demands on electricity grids.
arXiv:2509. 09794v5 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven analysis across building and urban energy systems.
arXiv:2609.13648v1 Announce Type: new Abstract: Solar energy decision support is fragmented across dashboards that provide data without explanation, research papers are slow to parse, and general-pur...
The review discusses how two‑phase heat transfer—critical for boiling, condensation, and thermal management—poses challenges for data reuse due to its complex interfacial physics. It surveys open datasets, machine‑learning techniques, and reusable software, organizing them with a spatial‑plus‑temporal dimensionality taxonomy (S+TD) that links data types to AI tasks such as regression, sequence learning, and image/video analysis. The paper proposes a roadmap for physics‑aware open data, including metadata standards, maturity labels, benchmark splits, and community databanks, emphasizing that progress in two‑phase AI relies as much on robust data infrastructure as on model design.
arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.
The paper introduces TiMi, a framework that enhances time series transformers with a Multimodal Mixture-of-Experts (MMoE) module to incorporate multimodal data, especially textual information, into forecasting. TiMi leverages large language models to generate future inferences that guide predictions, eliminating the need for explicit representation alignment. Experiments show TiMi achieves state‑of‑the‑art performance on sixteen real‑world multimodal forecasting benchmarks, outperforming advanced baselines while maintaining adaptability and interpretability.
arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.
arXiv:2606. 26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall.
arXiv:2507. 17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale.
The paper analyzes the environmental footprint of machine learning model training, focusing on large language models and their hardware. It finds that energy use and environmental impacts have risen exponentially over the past decade, even when employing carbon‑efficient electricity and more efficient hardware. The study argues that optimization strategies alone cannot curb these impacts due to a rebound effect, and stresses the need to evaluate hardware life‑cycle impacts and integrate environmental metrics into NLP research practices.
arXiv:2508. 12682v2 Announce Type: replace Abstract: The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital.
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.