| 1 |
The moderating role of renewable and non-renewable energy in environment-income nexus for ASEAN countries: Evidence from Method of Moments Quantile Regression
Anwar A, Siddique M, Dogan E, Sharif A
Renewable Energy, 164, 956, 2021 |
| 2 |
Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression
Dawar I, Dutta A, Bouri E, Saeed T
Renewable Energy, 163, 288, 2021 |
| 3 |
The renewable energy consumption-environmental degradation nexus in Top-10 polluted countries: Fresh insights from quantile-on-quantile regression approach
Sharif A, Mishra S, Sinha A, Jiao ZL, Shahbaz M, Afshan S
Renewable Energy, 150, 670, 2020 |
| 4 |
A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization
Hu JM, Tang JW, Lin YY
Renewable Energy, 149, 141, 2020 |
| 5 |
A dynamic alarm threshold setting method for photovoltaic array and its application
Yu C, Wang HZ, Yao JX, Zhao J, Sun Q, Zhu HL
Renewable Energy, 158, 13, 2020 |
| 6 |
Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm
Hu JM, Heng JN, Wen JM, Zhao WG
Renewable Energy, 162, 1208, 2020 |
| 7 |
Fault detection of wind turbines via multivariate process monitoring based on vine copulas
Xu QF, Fan ZH, Jia WY, Jiang CX
Renewable Energy, 161, 939, 2020 |
| 8 |
Probabilistic individual load forecasting using pinball loss guided LSTM
Wang Y, Gan DH, Sun MY, Zhang N, Lu ZX, Kang CQ
Applied Energy, 235, 10, 2019 |
| 9 |
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 |
| 10 |
A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data
Niemierko R, Toppel J, Trankler T
Applied Energy, 233, 691, 2019 |
