| 1 |
Smart grid lab research in Europe and beyond
Jansen LL, Andreadou N, Papaioannou I, Marinopoulos A
International Journal of Energy Research, 44(3), 1307, 2020 |
| 2 |
A deep neural network approach for behind-the-meter residential PV size, tilt and azimuth estimation
Mason K, Reno MJ, Blakely L, Vejdan S, Grijalva S
Solar Energy, 196, 260, 2020 |
| 3 |
A practical feature-engineering framework for electricity theft detection in smart grids
Razavi R, Gharipour A, Fleury M, Akpan IJ
Applied Energy, 238, 481, 2019 |
| 4 |
Predicting intra-day load profiles under time-of-use tariffs using smart meter data
Kiguchi Y, Heo Y, Weeks M, Choudhary R
Energy, 173, 959, 2019 |
| 5 |
Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management
Yilmaz S, Chambers J, Patel MK
Energy, 180, 665, 2019 |
| 6 |
Occupancy detection of residential buildings using smart meter data: A large-scale study
Razavi R, Gharipour A, Fleury M, Akpan IJ
Energy and Buildings, 183, 195, 2019 |
| 7 |
Assessing the equity and effectiveness of the GB energy price caps using smart meter data
Hardy A, Glew D, Gorse C
Energy Policy, 127, 179, 2019 |
| 8 |
First time real time incentive demand response program in smart grid with "i-Energy" management system with different resources
Eissa MM
Applied Energy, 212, 607, 2018 |
| 9 |
Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China
Fu X, Zeng XJ, Feng PP, Cai XW
Energy, 165, 76, 2018 |
| 10 |
Disaggregating high-resolution gas metering data using pattern recognition
Alzaatreh A, Mandjoubi L, Gething B, Sierra F
Energy and Buildings, 176, 17, 2018 |
