检索结果-南通市图书馆

Photogrammetry and deep learning for energy production prediction and building-integrated photovoltaics decarbonization

引用

Building Simulation 2024年第2期17卷 189-205页

作者： Ilyass Abouelaziz Youssef Jouane cesi lineact 7 bis Av.Robert Schuman51100ReimsFrance cesi lineact Parc Club des Tanneries 2 all Foulons67380StrasbourgFrance

Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient *** paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV *** method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation *** integrates photogrammetric data into practical BIM *** addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence *** primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying *** achieve this,the first step is to capture point cloud data of the building through photogrammetric *** data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan *** that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM ***,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire *** experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning *** objective is to identify the most suitable model for our specific ***,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software *** outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and *** extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new bui

关键词： building-integrated photovoltaic(BIPV) deep learning building information modeling photogrammetric building decarbonization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：