Zobrazeno 1 - 10
of 1 291
pro vyhledávání: '"Yan Jinyue"'
Autor:
Yu, Qing, Dong, Kechuan, Guo, Zhiling, Li, Jiaxing, Tan, Hongjun, Jin, Yanxiu, Yuan, Jian, Zhang, Haoran, Liu, Junwei, Chen, Qi, Yan, Jinyue
This research tackles the challenges of estimating Building-Integrated Photovoltaics (BIPV) potential across various temporal and spatial scales, accounting for different geographical climates and urban morphology. We introduce a holistic methodology
Externí odkaz:
http://arxiv.org/abs/2412.01291
Autor:
Chen, Dayin, Shi, Xiaodan, Jiang, Mingkun, Zhang, Haoran, Zhang, Dongxiao, Chen, Yuntian, Yan, Jinyue
Photovoltaic power forecasting (PVPF) is a critical area in time series forecasting (TSF), enabling the efficient utilization of solar energy. With advancements in machine learning and deep learning, various models have been applied to PVPF tasks. Ho
Externí odkaz:
http://arxiv.org/abs/2408.00601
Surpassing the two large emission sectors of transportation and industry, the building sector accounted for 34% and 37% of global energy consumption and carbon emissions in 2021, respectively. The building sector, the final piece to be addressed in t
Externí odkaz:
http://arxiv.org/abs/2406.04133
Autor:
Zhang, Shufan, Ma, Minda, Zhou, Nan, Yan, Jinyue, Feng, Wei, Yan, Ran, You, Kairui, Zhang, Jingjing, Ke, Jing
Buildings produce one-third of carbon emissions globally, however, data absence regarding global floorspace poses challenges in advancing building carbon neutrality. We compile the measured building stocks for 14 major economies and apply our global
Externí odkaz:
http://arxiv.org/abs/2406.04074
Autor:
Chen, Dayin, Shi, Xiaodan, Zhang, Haoran, Song, Xuan, Zhang, Dongxiao, Chen, Yuntian, Yan, Jinyue
Publikováno v:
IEEE Transactions on Mobile Computing,13 May 2024, 1 - 13
Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected r
Externí odkaz:
http://arxiv.org/abs/2404.10401
Autor:
Cao, Yuji, Zhao, Huan, Cheng, Yuheng, Shu, Ting, Chen, Yue, Liu, Guolong, Liang, Gaoqi, Zhao, Junhua, Yan, Jinyue, Li, Yun
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and high-level task
Externí odkaz:
http://arxiv.org/abs/2404.00282
Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. Spatially, there
Externí odkaz:
http://arxiv.org/abs/2401.00521
Publikováno v:
E3S Web of Conferences, Vol 238, p 00001 (2021)
Externí odkaz:
https://doaj.org/article/a0ef028cb45c41b382de7d02f0ff92b7
Autor:
Guo, Zhiling, Shi, Xiaodan, Zhang, Haoran, Huang, Dou, Song, Xiaoya, Yan, Jinyue, Shibasaki, Ryosuke
The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learnin
Externí odkaz:
http://arxiv.org/abs/2307.04101