| 1 |
Analysis of space heating demand in the Swiss residential building stock: Element-based bottom-up model of archetype buildings
Streicher KN, Padey P, Parra D, Burer MC, Schneider S, Patel MK
Energy and Buildings, 184, 300, 2019 |
| 2 |
Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes
Buttitta G, Turner WJN, Neu O, Finn DP
Energy and Buildings, 198, 84, 2019 |
| 3 |
Urban building energy modeling considering the heterogeneity of HVAC system stock: A case study on Japanese office building stock
Kim B, Yamaguchi Y, Kimura S, Ko Y, Ikeda K, Shimoda Y
Energy and Buildings, 199, 547, 2019 |
| 4 |
Assessment of the current thermal performance level of the Swiss residential building stock: Statistical analysis of energy performance certificates
Streicher KN, Padey P, Parra D, Burer MC, Patel MK
Energy and Buildings, 178, 360, 2018 |
| 5 |
Hierarchical calibration of archetypes for urban building energy modeling
Kristensen MH, Hedegaard RE, Petersen S
Energy and Buildings, 175, 219, 2018 |
| 6 |
Synthetic building stocks as a way to assess the energy demand and greenhouse gas emissions of national building stocks
Nageli C, Camarasa C, Jakob M, Catenazzi G, Ostermeyer Y
Energy and Buildings, 173, 443, 2018 |
| 7 |
Validation of a Bayesian-based method for defining residential archetypes in urban building energy models
Julia S, Carlos CD, Christoph FR
Energy and Buildings, 134, 11, 2017 |
| 8 |
Evaluation of life cycle carbon impacts for higher education building redevelopment: an archetype approach
Hawkins D, Mumovic D
Energy and Buildings, 147, 113, 2017 |
| 9 |
Comparison of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City
Cerezo C, Sokol J, AlKhaled S, Reinhart C, Al-Mumin A, Hajian A
Energy and Buildings, 154, 321, 2017 |
| 10 |
Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets
Davila CC, Reinhart CF, Bemis JL
Energy, 117, 237, 2016 |
