| 1 |
A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting
Wu ZC, Xiao LY
Energy, 183, 1178, 2019 |
| 2 |
Incorporate active learning to semi-supervised industrial fault classification
Yin LL, Wang HG, Fan WH, Kou L, Lin TY, Xiao YY
Journal of Process Control, 78, 88, 2019 |
| 3 |
Model Predictive Control with Active Learning Under Model Uncertainty: Why, When, and How
Heirung TAN, Paulson JA, Lee S, Mesbah A
AIChE Journal, 64(8), 3071, 2018 |
| 4 |
From experiential to research-based learning: The Renewable Energy Online (REO) master's program
Stroth C, Knecht R, Gunther A, Behrendt T, Golba M
Solar Energy, 173, 425, 2018 |
| 5 |
Dual adaptive model predictive control
Heirung TAN, Ydstie BE, Foss B
Automatica, 80, 340, 2017 |
| 6 |
Why not try active learning?
Falconer JL
AIChE Journal, 62(12), 4174, 2016 |
| 7 |
Prediction and optimization of wave energy converter arrays using a machine learning approach
Sarkar D, Contal E, Vayatis N, Dias F
Renewable Energy, 97, 504, 2016 |
| 8 |
Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning
Kou P, Liang DL, Gao L, Lou JY
Energy Conversion and Management, 89, 298, 2015 |
| 9 |
MPC-based dual control with online experiment design
Heirung TAN, Foss B, Ydstie BE
Journal of Process Control, 32, 64, 2015 |
| 10 |
Active learning strategy for smart soft sensor development under a small number of labeled data samples
Ge ZQ
Journal of Process Control, 24(9), 1454, 2014 |
