An energy-saving design method for residential building group based on convolutional neural network-Reference-Cited by-同舟云学术

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An energy-saving design method for residential building group based on convolutional neural network

Published:2024-04 Issue: Volume:82 Page:108291
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Zhang Yiming^ORCID,Li Ning,Zhao Tengfei,Li Ziwei

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference30 articles.

1. Influence of district features on energy consumption in non-domestic buildings;Choudhary;Build. Res. Inf.,2014

2. Habitable earth, carbon neutrality and global sustainable urbanization;Chen;J. Nat. Resour.,2022

3. China Building Energy Conservation Annual Development Research Report 2021 (Urban Housing Topic) [R],2021

4. Coupled CFD and building energy simulations for studying the impacts of building height topology and buoyancy on local urban micro climates;Allegrini;Urban Clim.,2017

5. GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database;Mutani;Energy Efficiency,2021

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. FloorDiffusion: Diffusion model-based conditional floorplan image generation method using parameter-efficient fine-tuning and image inpainting;Journal of Building Engineering;2024-10

2. Developing an integrated prediction model for daylighting, thermal comfort, and energy consumption in residential buildings based on the stacking ensemble learning algorithm;Building Simulation;2024-09-10

3. Comprehensive evaluation of natural ventilation potential of buildings in urban areas under the influence of multiple environment-related factors;Journal of Building Engineering;2024-07

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