| Short-term load and wind power forecasting using neural network-based prediction intervals H Quan, D Srinivasan, A Khosravi IEEE Transactions on Neural Networks and Learning Systems 25 (2), 303-315, 2014 | 718 | 2014 |
| An improved quantile regression neural network for probabilistic load forecasting W Zhang, H Quan, D Srinivasan IEEE Transactions on Smart Grid 10 (4), 4425-4434, 2018 | 233 | 2018 |
| A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources H Quan, D Srinivasan, AM Khambadkone, A Khosravi Applied energy 152, 71-82, 2015 | 196 | 2015 |
| Uncertainty handling using neural network-based prediction intervals for electrical load forecasting H Quan, D Srinivasan, A Khosravi Energy 73, 916-925, 2014 | 174 | 2014 |
| Particle swarm optimization for construction of neural network-based prediction intervals H Quan, D Srinivasan, A Khosravi Neurocomputing 127, 172-180, 2014 | 132 | 2014 |
| Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals H Quan, D Srinivasan, A Khosravi IEEE transactions on neural networks and learning systems 26 (9), 2123-2135, 2014 | 126 | 2014 |
| A survey of computational intelligence techniques for wind power uncertainty quantification in smart grids H Quan, A Khosravi, D Yang, D Srinivasan IEEE transactions on neural networks and learning systems 31 (11), 4582-4599, 2019 | 110 | 2019 |
| Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination W Zhang, H Quan, D Srinivasan Energy 160, 810-819, 2018 | 105 | 2018 |
| Reconciling solar forecasts: Geographical hierarchy D Yang, H Quan, VR Disfani, L Liu Solar Energy 146, 276-286, 2017 | 86 | 2017 |
| Improving probabilistic load forecasting using quantile regression NN with skip connections W Zhang, H Quan, O Gandhi, R Rajagopal, CW Tan, D Srinivasan IEEE Transactions on Smart Grid 11 (6), 5442-5450, 2020 | 82 | 2020 |
| Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study H Quan, D Srinivasan, A Khosravi Energy 103, 735-745, 2016 | 74 | 2016 |
| Reconciling solar forecasts: Temporal hierarchy D Yang, H Quan, VR Disfani, CD Rodríguez-Gallegos Solar Energy 158, 332-346, 2017 | 73 | 2017 |
| A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination W Zhang, O Gandhi, H Quan, CD Rodríguez-Gallegos, D Srinivasan Applied Energy 229, 96-110, 2018 | 57 | 2018 |
| Deep-learning-based probabilistic estimation of solar PV soiling loss W Zhang, S Liu, O Gandhi, CD Rodríguez-Gallegos, H Quan, ... IEEE Transactions on Sustainable Energy 12 (4), 2436-2444, 2021 | 48 | 2021 |
| Quality control for solar irradiance data D Yang, GM Yagli, H Quan 2018 IEEE innovative smart grid technologies-Asia (ISGT Asia), 208-213, 2018 | 38 | 2018 |
| Probabilistic solar irradiance transposition models H Quan, D Yang Renewable and Sustainable Energy Reviews 125, 109814, 2020 | 32 | 2020 |
| Outlier detection and data filling based on KNN and LOF for power transformer operation data classification D Zou, Y Xiang, T Zhou, Q Peng, W Dai, Z Hong, Y Shi, S Wang, J Yin, ... Energy Reports 9, 698-711, 2023 | 29 | 2023 |
| Construction of neural network-based prediction intervals using particle swarm optimization H Quan, D Srinivasan, A Khosravi The 2012 International Joint Conference on Neural Networks (IJCNN), 1-7, 2012 | 28 | 2012 |
| Transformer fault classification for diagnosis based on DGA and deep belief network D Zou, Z Li, H Quan, Q Peng, S Wang, Z Hong, W Dai, T Zhou, J Yin Energy Reports 9, 250-256, 2023 | 26 | 2023 |
| Construction of neural network-based prediction intervals for short-term electrical load forecasting H Quan, D Srinivasan, A Khosravi, S Nahavandi, D Creighton 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG), 66-72, 2013 | 18 | 2013 |
Dipti SrinivasanIEEE Fellow; Professor, National University ofВерификована је имејл адреса на nus.edu.sg
