arXiv subjects

Ruoyi Xu

Publications and source records attributed to Ruoyi Xu.

3 recordsLinked to original sources

arXiv2026

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization

Rapidly shifting operational scenarios driven by uncertain Distributed Energy Resource (DER) profiles render conventional distribution network optimization methods either computationally expensive or poorly generalizable. This paper introduces GridRAG, a pioneering retrieval-augmented framework that transforms optimization into a ``retrieve-and-refine'' paradigm. GridRAG first embeds scenario features and optimal solutions into a joint representation space to ensure semantic consistency. Based on the hybrid semantic information, the similar historical scenarios are then retrieved from a pre-constructed database. Then an SDEdit-style diffusion module is integrated to refine retrieved solutions by modeling the conditional distribution over near-feasible manifolds. This process effectively pulls retrieved solutions into near-optimal attraction basins, providing a high-quality warm-start for the final solver. Validated on three optimization tasks across four standard topologies, GridRAG demonstrates superior cross-scenario generalization and a multi-fold speedup in solution time compared to existing learning-based and model-based baselines. Our code is available at https://github.com/YuxuanCEE/GridRAG.

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arXiv2026

Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting

Load forecasting is pivotal for stable power systems. Conventional uni-modal methods suffer from representation drift under data scarcity. While recent multi-modal approaches attempt to alleviate this, they exhibit severe information redundancy, merely recycling time series data via superficial intra-modal transformations. In this paper, we argue that the essence of multi-modal time series learning should expand representation manifolds via complementary cross-modal knowledge enrichment rather than duplicating redundant information, especially for few-shot scenarios prevalent in power systems. To this end, we propose KEMM-Net, a Knowledge-Enriched Multi-Modal Network for power load forecasting. KEMM-Net first constructs textual and visual embeddings to strengthen load time series representations from different knowledge perspectives. It then introduces a Partial Information Decomposition (PID)-guided cross-modal contrastive learning mechanism to achieve cross-modal semantic alignment and balance redundant, synergistic, and unique information for forecasting. Extensive experiments on real-world public datasets demonstrate that KEMM-Net consistently outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings. Our code is available at https://anonymous.4open.science/r/KEMM-Net-2898.

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arXiv2023

Improving Emotional Expression and Cohesion in Image-Based Playlist Description and Music Topics: A Continuous Parameterization Approach

Text generation in image-based platforms, particularly for music-related content, requires precise control over text styles and the incorporation of emotional expression. However, existing approaches often need help to control the proportion of external factors in generated text and rely on discrete inputs, lacking continuous control conditions for desired text generation. This study proposes Continuous Parameterization for Controlled Text Generation (CPCTG) to overcome these limitations. Our approach leverages a Language Model (LM) as a style learner, integrating Semantic Cohesion (SC) and Emotional Expression Proportion (EEP) considerations. By enhancing the reward method and manipulating the CPCTG level, our experiments on playlist description and music topic generation tasks demonstrate significant improvements in ROUGE scores, indicating enhanced relevance and coherence in the generated text.

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