| Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory Y He, R Liu, H Li, S Wang, X Lu Applied energy 185, 254-266, 2017 | 189 | 2017 |
| Probability density forecasting of wind power using quantile regression neural network and kernel density estimation Y He, H Li Energy conversion and management 164, 374-384, 2018 | 186 | 2018 |
| Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network Y He, Y Qin, S Wang, X Wang, C Wang Applied energy 233, 565-575, 2019 | 157 | 2019 |
| Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function Y He, Q Xu, J Wan, S Yang Energy 114, 498-512, 2016 | 132 | 2016 |
| Short-term wind power prediction based on EEMD–LASSO–QRNN model Y He, Y Wang Applied Soft Computing 105, 107288, 2021 | 124 | 2021 |
| Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling YY He, JZ Zhou, XQ Xiang, H Chen, H Qin Chaos, Solitons & Fractals 42 (5), 3169-3176, 2009 | 102 | 2009 |
| Reservoir flood control operation based on chaotic particle swarm optimization algorithm Y He, Q Xu, S Yang, L Liao Applied Mathematical Modelling 38 (17-18), 4480-4492, 2014 | 98 | 2014 |
| Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression Y He, H Li, S Wang, X Yao Neurocomputing 430, 121-137, 2021 | 87 | 2021 |
| Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function Y He, Y Zheng Energy 154, 143-156, 2018 | 81 | 2018 |
| A power load probability density forecasting method based on RBF neural network quantile regression Y He, Q Xu, S Yang, B Yu Zhongguo Dianji Gongcheng Xuebao(Proceedings of the Chinese Society of …, 2013 | 72* | 2013 |
| Particle swarm optimization-based neural network model for short-term load forecasting N Lu, J Zhou, Y He Power System Protection and Control 38 (12), 65-68, 2010 | 68* | 2010 |
| Optimal PID governor tuning of hydraulic turbine generators with bacterial foraging particle swarm optimization algorithm KOU Pan-Gao, Z Jian-Zhong, HE Yao-Yao, X Xiu-qiao, LI Chao-shun Proceedings of the CSEE 26, 2009 | 64* | 2009 |
| Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression Y He, Y Yan, Q Xu International Journal of Electrical Power & Energy Systems 113, 515-527, 2019 | 63 | 2019 |
| Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach Q Xu, M Li, C Jiang, Y He Physica A: Statistical Mechanics and its Applications 534, 122173, 2019 | 59 | 2019 |
| A fuzzy clustering iterative model using chaotic differential evolution algorithm for evaluating flood disaster Y He, J Zhou, P Kou, N Lu, Q Zou Expert Systems with Applications 38 (8), 10060-10065, 2011 | 50 | 2011 |
| Probability density forecasting of wind power based on multi-core parallel quantile regression neural network Y He, W Zhang Knowledge-Based Systems 209, 106431, 2020 | 45 | 2020 |
| Weighted quantile regression via support vector machine Q Xu, J Zhang, C Jiang, X Huang, Y He Expert Systems with Applications 42 (13), 5441-5451, 2015 | 43 | 2015 |
| Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods Y He, Y Zheng, Q Xu Measurement 136, 579-593, 2019 | 42 | 2019 |
| A study on short-term power load probability density forecasting considering wind power effects Y He, Y Qin, X Lei, N Feng International Journal of Electrical Power & Energy Systems 113, 502-514, 2019 | 40 | 2019 |
| Short-term power load probability density forecasting method based on real time price and support vector quantile regression Y He, R Liu, A Han Proceedings of the CSEE 37 (3), 768-776, 2017 | 40* | 2017 |