| 1 |
Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?
Li FGN, Bataille C, Pye S, O'Sullivan A
Applied Energy, 239, 991, 2019 |
| 2 |
Parameter estimation in reactive systems subject to sufficient criteria for thermodynamic stability
Glass M, Mitsos A
Chemical Engineering Science, 197, 420, 2019 |
| 3 |
Unique Building Identifier: A natural key for building data matching and its energy application
Wang N, Vlachokostas A, Borkum M, Bergmann H, Zaleski S
Energy and Buildings, 184, 230, 2019 |
| 4 |
On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives
Carpino C, Mora D, De Simone M
Energy and Buildings, 186, 297, 2019 |
| 5 |
Wind missing data arrangement using wavelet based techniques for getting maximum likelihood
Zapata-Sierra AJ, Cama-Pinto A, Montoya FG, Alcayde A, Manzano-Agugliaro F
Energy Conversion and Management, 185, 552, 2019 |
| 6 |
Analysis of wind energy potential; A case study of Kocaeli University campus
Caglayan I, Tikiz I, Turkmen AC, Celik C, Soyhan G
Fuel, 253, 1333, 2019 |
| 7 |
Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow
Nutkiewicz A, Yang Z, Jain RK
Applied Energy, 225, 1176, 2018 |
| 8 |
Influences of energy data on Bayesian calibration of building energy model
Lim H, Zhai ZQ
Applied Energy, 231, 686, 2018 |
| 9 |
The data-driven schedule of wind farm power generations and required reserves
Long H, Zhang ZJ, Sun MX, Li YF
Energy, 149, 485, 2018 |
| 10 |
Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata
Deng HF, Fannon D, Eckelman MJ
Energy and Buildings, 163, 34, 2018 |
