| 1 |
Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network
He YY, Qin Y, Wang S, Wang X, Wang C
Applied Energy, 233, 565, 2019 |
| 2 |
Power load probability density forecasting using Gaussian process quantile regression
Yang YD, Li SF, Li WQ, Qu MJ
Applied Energy, 213, 499, 2018 |
| 3 |
Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function
He YY, Zheng YY
Energy, 154, 143, 2018 |
| 4 |
A deep learning model for short-term power load and probability density forecasting
Guo ZF, Zhou KL, Zhang XL, Yang SL
Energy, 160, 1186, 2018 |
| 5 |
Probability density forecasting of wind power using quantile regression neural network and kernel density estimation
He YY, Li HY
Energy Conversion and Management, 164, 374, 2018 |
| 6 |
Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory
He YY, Liu R, Li HY, Wang S, Lu XF
Applied Energy, 185, 254, 2017 |
| 7 |
Household electricity demand forecasting using adaptive conditional density estimation
Amara F, Agbossou K, Dube Y, Kelouwani S, Cardenas A, Bouchard J
Energy and Buildings, 156, 271, 2017 |
| 8 |
Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function
He YY, Xu QF, Wan JH, Yang SL
Energy, 114, 498, 2016 |
| 9 |
Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models
Taylor JW, McSharry PE, Buizza R
IEEE Transactions on Energy Conversion, 24(3), 775, 2009 |
