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A real scene 3D Model-Driven sunlight analysis method for complex building roofs

journal contribution
posted on 2024-12-16, 16:33 authored by Jinghai Xu, Mengxuan Qi, Haoran Jing, Craig HancockCraig Hancock, Peng Qiao, Nan Shen
A real-scene 3D model of complex buildings, derived from UAV (Unmanned Aerial Vehicle) surveys, can significantly improve the accuracy of sunlight analysis for the arrangement of photovoltaic panels. We propose a method for sunlight analysis of complex building roofs driven by the real-scene 3D model, which includes generating and optimizing the 3D model and a parameterized sunlight analysis algorithm. The generation and optimization method involves: reducing the number of model meshes by selecting a lower level of detail and proposing a mesh simplification algorithm to simplify the model; reconstructing the structure of the model meshes to smooth them and solve the pseudo-occlusion problems caused by the model's triangular structures by transforming triangular meshes into quadrilateral meshes; improving the accuracy of the obstacles’ 3D models on the roof by completing high-precision obstacle modeling and superimposing it on the simplified model. Subsequently, a parameterized sunlight analysis algorithm suited to the optimized 3D model is presented based on the Grasshopper parameterized software platform. We design a complete set of sunlight analysis algorithm programs by exploring the geographical location, time range, time step, and other parameters of the real-scene 3D model. Finally, the method's feasibility is verified through a case study of a complex building.

Funding

The National Natural Science Foundation of China (Grant No. 42271420)

Natural Science Foundation of Jiangsu Province (Grant No. BK20220367)

Science and Technology Plan Project of the Ministry of Housing and Urban-Rural Development of China (Grant No. 2022-K-041)

History

School

  • Architecture, Building and Civil Engineering

Published in

Energy and Buildings

Volume

325

Issue

2024

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Acceptance date

2024-11-10

Publication date

2024-11-13

Copyright date

2024

ISSN

0378-7788

eISSN

1872-6178

Language

  • en

Depositor

Dr Craig Hancock. Deposit date: 12 December 2024

Article number

115051

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