LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES
Xiana Chen , Wei Tu , Junxian Yu , Rui Cao , Shengao Yi , Qingquan Li
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引用次数: 0

Abstract

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.281011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

通过结合物理建模、机器学习和 3D 建筑,进行基于 LCZ 的全城太阳辐射潜力分析
应对气候变化和城市能源问题是一项巨大挑战。光伏建筑一体化(BIPV)在节能和减少碳排放方面发挥着举足轻重的作用。然而,利用物理模型评估建筑物太阳辐射的传统方法计算密集且耗时。本研究提出了一种混合方法,将基于物理模型的太阳辐射计算与机器学习(ML)相结合,用于城市范围内的建筑物太阳辐射潜力(SRP)分析。通过考虑城市形态、土地覆盖和气象特征,对局部气候区(LCZ)进行了分类。利用计算密集型物理模型与三维建筑模型相结合,对具有代表性的 LCZ 的太阳辐射势进行精确评估。然后开发了一个 ML 模型,用于有效预测全市建筑物屋顶和外墙的 SRP。在中国深圳进行了一项实验,以验证所提出的方法。结果表明,深圳每年的建筑物太阳辐射总量为 3.28∗1011kwh。罗湖区的太阳辐射量密度最高。基于低密度区的分析表明,紧凑型低密度区为屋顶提供了更大的太阳辐射量,而紧凑型高层低密度区则为外墙提供了更大的太阳辐射量。此外,BIPV 每年可减少多达 4185 万吨的二氧化碳排放量。值得注意的是,在深圳,仅在屋顶安装太阳能光伏发电设备,就能满足深圳市电力部门 87.81% 的碳减排目标。这项研究通过物理建模和 ML 的结合,为城市范围内的 SRP 估算提供了一种替代方法,并为低碳城市中数据驱动和模型驱动的城市规划和管理提供了宝贵的见解。
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来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
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