太阳成8722(中国)有限公司-GREEN NO.1

标题: Road PV production estimation at city scale: A predictive model towards feasible assessing regional energy generation from solar roads

作者: Liu, ZY (Liu, Ziyu); Fei, T (Fei, Teng)

来源出版物: JOURNAL OF CLEANER PRODUCTION 卷: 321 文献号: 129010 DOI: 10.1016/j.jclepro.2021.129010 出版年: OCT 25 2021

摘要: Solar roads are roads embedded with solar panels which can converting solar energy radiated on the road into storable electricity. Over the recent years, pioneering solar road prototypes were tested in different regions around the world. Driven by demand, road photovoltaic production calculation, based on street view images (SVI) has been proposed (Liu et al., 2019). However, in addition to the high runtime overhead, this method cannot be applied to cities in which recent SVI are unavailable. At the meantime, researches on the estimation of road photovoltaic production of cities are rare, especially ones with spatially explicit inferences. This study proposes an innovative predictive model that can estimate road photovoltaic capacity of cities with urban features obtained from remote sensing images and other multi-source GIS data. As a scaffolding step, accurate estimation of potential road PV in 27 cities were calculated using SVI. Compared with the SVI approach, our predictive model is fast, robust and yet accurate as well. As a result, the spatial distribution of the potential energy production of solar roads for the 27 cities are mapped, which provides insights into which area should be prioritized for building solar roads. By analysing and comparing the estimated results and current vehicle energy demand, we propose different suggestions for the construction of photovoltaic roads for different types of cities. These suggestions may provide support for urban solar road planning in the course of adapting to cleaner energy sources. Additionally, data required by this predictive model is easy to access, which contributes to the universal applicability of this method.

作者关键词: Photovoltaic road; Remote sensing; Street view images; Transportation energy

地址: [Liu, Ziyu; Fei, Teng] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

通讯作者地址: Fei, T (通讯作者)，Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: feiteng@whu.edu.cn

影响因子：9.297