Conventional numerical weather prediction is limited by high computational cost and delayed availability, while single-path models cannot effectively integrate multi-scale historical power information with future meteorological drivers. To address these issues, this work proposes a dual-path ultra-short-term wind power forecasting method based on Pangu-Weather and dynamic gated attention fusion. The raw wind power series is decomposed using complete ensemble empirical mode decomposition with adaptive noise, and meteorological forecasts are generated by Pangu-Weather. In the proposed network, the power path uses extreme gradient boosting to model short-window historical power features. The meteorological path comprises a temporal convolutional network branch and a meteorological feature branch, which respectively extract long-range temporal features and future meteorological driving features. A dynamic gated attention fusion module is then designed to fuse the outputs of the two paths through step-wise adaptive weighting, inter-step dependency modeling, and residual correction. Experiments on a real-world 387.45 MW wind farm demonstrate that the proposed model achieves an root mean square error of 21.28 MW, outperforming the best baseline by 13.97% and surpassing several baseline models overall. Ablation studies further validate the necessity of the dual-path design and the dynamic fusion mechanism. These results demonstrate that AI-weather-driven forecasting and dual-path dynamic fusion can provide a promising low-latency meteorological-input strategy for ultra-short-term wind power forecasting.
Lejia Zhu, Yujia Zhang, Qiang Wang et al.· Clean Energy· 0 citations
Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state. We present SciForge, a multimodal research-native AI workbench that reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular agent-accessible services. SciForge is built around five pillars: (i) \emph{goal-scoped scientific decision governance} for \textbf{goal-oriented} research, with review gates and shared review surfaces; (ii) \emph{translate-then-reason} for \textbf{multimodal} input, routing scientific objects through domain translators before the agent reasons; (iii) \emph{evidence governance} for \textbf{auditable} traceability, linking claims to provenance chains and audit findings; (iv) \emph{collaborative team science} for \textbf{collaborative} research, enabling multi-role decision governance, with shared team workspaces planned for future releases; and (v) \emph{real-world application scenarios} for \textbf{practical} impact, demonstrated through eight end-to-end user cases, with flagship demonstrations including multi-day agentic research sprints for gene discovery, AI-guided de novo protein design, molecular optimization, and genome-to-BGC discovery. The system combines a thin interaction layer, contextual research capability patterns, an Agent Runtime and Workflow Engine, an Evidence-DAG audit sidecar and a Scientific Model Router. SciForge currently runs as a desktop application, with mobile supervision support; future releases will deepen team collaboration. The system is open-source and available at https://github.com/AGI4Sci/SciForge
SciForge Team Zhangyang Gao, Minghao Fang, Yifei Liu et al.· 0 citations
