arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.
By Jiahui Huang, Ao Luo, Lei Liu, Hongwei Zhao, Tengyuan Liu, Ruibo Guo, Bo Wang, Zhao Wang, Bin Li
arXiv:2609.24444v1 Announce Type: new
Abstract: Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market o...
By Yuhan Zhu, Jilin Hu, Xinying Cai, Yingshan Li, Li Ma, Xiangfei Qiu Linsen Li, Kai Zhang, Yao Fu, Weihao Jiang, Bin Yang
arXiv:2607. 00051v1 Announce Type: cross Abstract: Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation.
By Ahmadreza Chokhachian, V. Roshan Joseph, Yu Ding
arXiv:2607. 01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation.
By Ronghui Xu, Tongxin Wu, Guozhen Zhang, Yihan Li, Chenjuan Guo, Bin Yang, Yong Li
arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.
By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
By Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen