A review of bottom-up building stock models for energy consumption in the residential sector-Reference-Cited by-同舟云学术

A review of bottom-up building stock models for energy consumption in the residential sector

Published:2010-07 Issue:7 Volume:45 Page:1683-1697
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Kavgic M.,Mavrogianni A.,Mumovic D.,Summerfield A.,Stevanovic Z.,Djurovic-Petrovic M.

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference75 articles.

1. International Energy Agency (IEA). Energy consumption by sector [Online]. Available: http://www.eia.doe.gov/emeu/aer/pdf/pages/sec2.pdf [accessed 21.12.09].

2. Directive of the European parliament and of the council on the energy performance of buildings;EPBD Directive 2002/91/EC;The European Community Official Journal,2003

3. Department of Energy and Climate Change. Climate change act 2009 impact assessment; March 2009.

4. Building a low-carbon economy – the UK's contribution to tackling climate change;Committee on Climate Change (CCC),2008

5. Technical options and strategies for decarbonizing UK housing;Lowe;Building Research & Information,2007

Cited by 646 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. Circular Economy Policies for Decarbonization of US Commercial Building Stocks: Data Integration and System Dynamics Coflow Modeling Approach;Journal of Management in Engineering;2024-03

2. Combined top-down and bottom-up approach for CO2 emissions estimation in building sector of beijing: Taking new energy vehicles into consideration;Energy;2024-03

3. A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets;Applied Energy;2024-03

4. Community stochastic domestic electricity forecasting;Applied Energy;2024-02

5. UAV-based automated 3D modeling framework using deep learning for building energy modeling;Sustainable Cities and Society;2024-02