以居住小区为研究对象,首先借助Ladybug模拟小区建筑屋面、墙面光伏发电;之后通过机器学习训练出能在0.5 s内预测高度组合不同的整个小区建筑光伏发电量的模型;最后借助遗传算法,以最大建筑光伏发电总量为目标,对小区建筑高度组合进行寻优计算。研究发现：小区内建筑高度的合理组合能有效提升其光伏发电潜力,在建筑高度控制曲面s朝南偏西倾斜且以西南侧为中心下凹时,长沙地区参数化小区能获得最高全年光伏发电量（838297 kWh）。对于全国具有不同光气候条件和纬度的地区,通过所提方法可计算出差异性的居住小区最佳高度组合。
Abstract
This study focuses on residential communities and begins by using Ladybug to simulate photovoltaic generation on rooftops and walls of buildings within the community. Subsequently, a machine learning model is trained to predict the total photovoltaic generation of the entire community with different height combinations within 0.5 seconds. Finally, a genetic algorithm is utilized to optimize the height combinations of the buildings in the community with the objective of maximizing the total photovoltaic generation. The findings indicate that a reasonable combination of building heights can effectively enhance the photovoltaic potential of the community. Specifically, when the building height control surface tilts towards the west-southwest direction and exhibits a concave shape centered on the southwest side, the parameterized community in Changsha achieves the highest annual photovoltaic generation of 838297 kWh. For regions across the country with varying climatic conditions and latitudes, the method described in this study can be used to calculate optimal height combinations for residential communities.
关键词
太阳能 /
光伏发电 /
机器学习 /
遗传算法 /
建筑设计
Key words
solar energy /
photovoltaic power /
machine learning /
genetic algorithms /
architectural design
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 王君, 余本东, 王矗垚, 等. 太阳能光伏光热建筑一体化(BIPV/T)研究新进展[J]. 太阳能学报, 2022, 43(6): 72-78.
WANG J, YU B D, WANG C Y, et al.New advancements of building integrated photovoltaic/thermal system(BIPV/T)[J]. Acta energiae solaris sinica, 2022, 43(6): 72-78.
[2] 刘艳峰, 王玥, 王登甲, 等. 屋顶光伏发电与遮阳综合节能分析[J]. 太阳能学报, 2019, 40(6): 1545-1552.
LIU Y F, WANG Y, WANG D J, et al.Study on comprehensive energy-saving of shading and photovoltaic performance of roof added PV module[J]. Acta energiae solaris sinica, 2019, 40(6): 1545-1552.
[3] HORVÁTH M, KASSAI-SZOÓ D, CSOKNYAI T. Solar energy potential of roofs on urban level based on building typology[J]. Energy and buildings, 2016, 111: 278-289.
[4] PEREZ R, DAVID M, HOFF T E, et al.Spatial and temporal variability of solar energy[J]. Foundations and trends® in renewable energy, 2016, 1(1): 1-44.
[5] LUTHANDER R, WIDÉN J, NILSSON D, et al. Photovoltaic self-consumption in buildings: a review[J]. Applied energy, 2015, 142: 80-94.
[6] 罗淑湘, 牛彦涛, 赵鹏, 等. 基于GIS的区域建筑屋顶太阳能资源精细化评估技术研究[J]. 建筑技术, 2018, 49(9): 1008-1010.
LUO S X, NIU Y T, ZHAO P, et al.Research on precise assessment technology of regional building roof solar energy resource based on GIS[J]. Architecture technology, 2018, 49(9): 1008-1010.
[7] 江云婷, 孙英君, 王鸿燕, 等. 基于屋顶与竖墙面积的太阳能光伏潜力评估方法综述[J]. 测绘与空间地理信息, 2019, 42(9): 81-84.
JIANG Y T, SUN Y J, WANG H Y, et al.Review of solar photovoltaic potential evaluation methods based on roof and facade area[J]. Geomatics & spatial information technology, 2019, 42(9): 81-84.
[8] 张节潭, 李春来, 杨立滨, 等. 基于GIS自动化的区域建筑屋顶太阳能风能资源精细化评估技术[J]. 制造业自动化, 2020, 42(6): 146-149, 156.
ZHANG J T, LI C L, YANG L B, et al.Based on GIS automation, the refined evaluation technology of solar energy and wind energy resources on the roof of regional buildings[J]. Manufacturing automation, 2020, 42(6): 146-149, 156.
[9] 朱姝妍, 马辰龙, 向科. 优化算法驱动的建筑生成设计实践研究[J]. 南方建筑, 2021(1): 7-14.
ZHU S Y, MA C L, XIANG K.Research on the architectural generative design practice driven by the optimization algorithm[J]. South architecture, 2021(1): 7-14.
[10] 马辰龙, 朱姝妍, 王明洁. 机器学习技术在建筑设计中的应用研究[J]. 南方建筑, 2021(2): 121-131.
MA C L, ZHU S Y, WANG M J.Applied research of machine learning technique in architectural design[J]. South architecture, 2021(2): 121-131.
[11] 张华. 城市建筑屋顶光伏利用潜力评估研究[D]. 天津: 天津大学, 2017.
ZHANG H.Research on PV energy potential of rooftop in urban area[D]. Tianjin: Tianjin University, 2017.
[12] 聂梅生, 秦佑国, 江亿. 中国绿色低碳住区技术评估手册[M]. 5/2011版. 北京: 中国建筑工业出版社, 2011.
NIE M S, QIN Y G, JIANG Y.Technical assessment manual for green and low-carbon residential areas in China[M]. version 5/2011. Beijing: China Architecture & Building Press, 2011.
[13] 肖婧, 刘化高, 王希嘉. 面向“碳中和”目标的未来社区建设探索[J]. 中外建筑, 2022(6): 85-89.
XIAO J, LIU H G, WANG X J.Research on the future community construction for the goal of carbon neutrality[J]. Chinese & overseas architecture, 2022(6): 85-89.
[14] 贾凌云, 云斯宁, 赵泽妮, 等. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报, 2022, 43(12): 88-97.
JIA L Y, YUN S N, ZHAO Z N, et al.Recent progress of short-term forecasting of photovoltaic generation based on artificial neural networks[J]. Acta energiae solaris sinica, 2022, 43(12): 88-97.
基金
中建股份科技研发课题（CSCEC-2022-Z-10）; 中建五局科技研发课题 (cscec5b-2022-09)
PDF(2531 KB)
