| 1 |
Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods
Cui J, Yu RZ, Zhao DB, Yang JY, Ge WC, Zhou XM
Applied Energy, 247, 480, 2019 |
| 2 |
A parametric bootstrap algorithm for cluster number determination of load pattern categorization
Luo X, Zhu X, Lim EG
Energy, 180, 50, 2019 |
| 3 |
A hybrid price-based demand response program for the residential micro-grid
Monfared HJ, Ghasemi A, Loni A, Marzband M
Energy, 185, 274, 2019 |
| 4 |
Clustering district heat exchange stations using smart meter consumption data
Tureczek AM, Nielsen PS, Madsen H, Brun A
Energy and Buildings, 182, 144, 2019 |
| 5 |
GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system
Lu YK, Tian Z, Peng P, Niu JD, Li WC, Zhang HJ
Energy and Buildings, 190, 49, 2019 |
| 6 |
Optimal TOU tariff design using robust intuitionistic fuzzy divergence based thresholding
Charwand M, Gitizadeh M
Energy, 147, 655, 2018 |
| 7 |
Clustering-based analysis for residential district heating data
Gianniou P, Liu XF, Heller A, Nielsen PS, Rode C
Energy Conversion and Management, 165, 840, 2018 |
| 8 |
Electric load shape benchmarking for small- and medium-sized commercial buildings
Luo X, Hong TZ, Chen YX, Piette MA
Applied Energy, 204, 715, 2017 |
| 9 |
Effects of the geothermal load on the ground temperature recovery in a ground heat exchanger
Baek SH, Yeo MS, Kim KW
Energy and Buildings, 136, 63, 2017 |
| 10 |
Constructing electricity load profile and formulating load pattern for urban apartment in Korea
Seo YK, Hong WH
Energy and Buildings, 78, 222, 2014 |
