Exploring variance in residential electricity consumption: Household features and building properties-Reference-Cited by-同舟云学术

Exploring variance in residential electricity consumption: Household features and building properties

Published:2012-04 Issue: Volume:92 Page:637-643
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Bartusch Cajsa,Odlare Monica,Wallin Fredrik,Wester Lars

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference14 articles.

1. Commission of the European Communities. 20 20 by 2020: Europe’s climate change opportunity. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. COM (2008) 30 final. Brussels: Commission of the European Communities; 2008.

2. Statens Offentliga Utredningar. Ett energieffektivare Sverige: Delbetänkande av Energieffektiviseringsutredningen. SOU 2008:25. Stockholm: SOU; 2008.

3. Statens Offentliga Utredningar. Vägen till ett energieffektivare Sverige: Slutbetänkande av Energieffektiviseringsutredningen. SOU 2008: 110. Stockholm: SOU; 2008.

4. Energimyndigheten. Energiläget 2009. ET 2009:28. Eskilstuna: Energimyndigheten; 2009.

5. Swedenergy. The electricity year 2009. Stockholm: Swedenergy; 2010.

Cited by 100 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. Energy ratings as drivers of energy sufficiency in residential buildings: A comprehensive review and future directions;Energy and Buildings;2024-10

2. Who heats and cools? Access to residential heating and cooling in Northern California and implications for energy transitions;Energy Policy;2024-08

3. Determination of household electricity expenditures using quantile regression with Kennedy approach;Energy Efficiency;2024-03-19

4. Determinants of Urban Residential Electricity Consumption in Burkina Faso: A Study of Dwelling, Household, Socio-Economic and Appliance-Related Factors;Buildings;2024-03-05

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