| 1 |
Hybrid deep neural model for hourly solar irradiance forecasting
Huang X, Li Q, Tai YH, Chen ZQ, Zhang J, Shi JS, Gao BX, Liu WM
Renewable Energy, 171, 1041, 2021 |
| 2 |
Black tea classification employing feature fusion of E-Nose and E-Tongue responses
Banerjee MB, Roy RB, Tudu B, Bandyopadhyay R, Bhattacharyya N
Journal of Food Engineering, 244, 55, 2019 |
| 3 |
Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks
Liu H, Mi XW, Li YF
Energy Conversion and Management, 155, 188, 2018 |
| 4 |
Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network
Liu H, Mi XW, Li YF
Energy Conversion and Management, 166, 120, 2018 |
| 5 |
An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm
Liu H, Mi XW, Li YF
Renewable Energy, 123, 694, 2018 |
| 6 |
An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization
Yin H, Dong Z, Chen YL, Ge JF, Lai LL, Vaccaro A, Meng AN
Energy Conversion and Management, 150, 108, 2017 |
| 7 |
Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm
Mi XW, Liu H, Li YF
Energy Conversion and Management, 151, 709, 2017 |
| 8 |
Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm
Meng AB, Ge JF, Yin H, Chen SZ
Energy Conversion and Management, 114, 75, 2016 |
| 9 |
Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor
Gan C, Wu JH, Yang SY, Hu YH, Cao WP
IEEE Transactions on Energy Conversion, 31(1), 303, 2016 |
| 10 |
Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks
Liu H, Tian HQ, Liang XF, Li YF
Applied Energy, 157, 183, 2015 |
