An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms
Authors:
- 16 September 2024
- journal article
- research article
- Published by Springer Nature in Building Simulation
- Vol. 17 (11) , 2017-2042
Abstract
No abstract availableKeywords
This publication has 80 references indexed in Scilit:
- Modeling and management performances of distributed energy resource for demand flexibility in Japanese zero energy houseBuilding Simulation, 2023
- Design and performance testing of a novel building integrated photovoltaic thermal roofing panelBuilding Simulation, 2023
- The impact of urban morphology on multiple ecological effects: Coupling relationships and collaborative optimization strategiesBuilding Simulation, 2023
- AutoBPS-BIM: A toolkit to transfer BIM to BEM for load calculation and chiller design optimizationBuilding Simulation, 2023
- Influence of urban morphological factors on building energy consumption combined with photovoltaic potential: A case study of residential blocks in central ChinaBuilding Simulation, 2023
- A method of determining typical meteorological year for evaluating overheating performance of passive buildingsBuilding Simulation, 2023
- Influence of built environment on building energy consumption: a case study in Nanjing, ChinaEnvironment, Development and Sustainability, 2023
- Building a greener future—Progress of the green building technology in the “13th Five-Year Plan” of ChinaBuilding Simulation, 2022
- Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasetsBuilding Simulation, 2022
- Performance-driven optimization of urban open space configuration in the cold-winter and hot-summer region of ChinaBuilding Simulation, 2019