| 1 |
Data-driven classification of residential energy consumption patterns by means of functional connectivity networks
Markovic R, Gosak M, Grubelnik V, Marhl M, Virtic P
Applied Energy, 242, 506, 2019 |
| 2 |
Heterogeneous impacts of households on carbon dioxide emissions in Chinese provinces
Zhang JJ, Yu BY, Wei YM
Applied Energy, 229, 236, 2018 |
| 3 |
Statistical methodology to assess changes in the electrical consumption profile of buildings
Serrano-Guerrero X, Escriva-Escriva G, Roldan-Blay C
Energy and Buildings, 164, 99, 2018 |
| 4 |
Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions
Li GN, Hu YP, Chen HX, Li HR, Hu M, Guo YB, Liu JY, Sun SB, Sun M
Applied Energy, 185, 846, 2017 |
| 5 |
Modeling the carbon consequences of pro-environmental consumer behavior
Duarte R, Feng KS, Hubacek K, Sanchez-Choliz J, Sarasa C, Sun LX
Applied Energy, 184, 1207, 2016 |
| 6 |
Energy and climate hand-in-hand: Financing RES-E support with carbon revenues
Verde SF, Pazienza MG
Energy Policy, 88, 234, 2016 |
| 7 |
Determinants of greenhouse gas emissions from Swedish private consumption: Time-series and cross-sectional analyses
Nassen J
Energy, 66, 98, 2014 |
| 8 |
Social groups and CO2 emissions in Spanish households
Duarte R, Mainar A, Sanchez-Choliz J
Energy Policy, 44, 441, 2012 |
| 9 |
Needs, resources and climate change: Clean and efficient conversion technologies
Ghoniem AF
Progress in Energy and Combustion Science, 37(1), 15, 2011 |
| 10 |
Life cycle inventory (LCI) of different forms of rice consumed in households in Japan
Roy P, Ijiri T, Nei D, Orikasa T, Okadome H, Nakamura N, Shiina T
Journal of Food Engineering, 91(1), 49, 2009 |
