| Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression H Liu, X Mi, Y Li, Z Duan, Y Xu Renewable energy 143, 842-854, 2019 | 163 | 2019 |
| A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting H Liu, C Yu, H Wu, Z Duan, G Yan Energy 202, 117794, 2020 | 151 | 2020 |
| A review on multi-objective optimization framework in wind energy forecasting techniques and applications H Liu, Y Li, Z Duan, C Chen Energy Conversion and Management 224, 113324, 2020 | 149 | 2020 |
| Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm H Liu, Z Duan, F Han, Y Li Energy Conversion and Management 156, 525-541, 2018 | 116 | 2018 |
| Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy Y Li, H Shi, F Han, Z Duan, H Liu Renewable energy 135, 540-553, 2019 | 113 | 2019 |
| Intelligent modeling strategies for forecasting air quality time series: A review H Liu, G Yan, Z Duan, C Chen Applied Soft Computing 102, 106957, 2021 | 103 | 2021 |
| Air PM2. 5 concentration multi-step forecasting using a new hybrid modeling method: comparing cases for four cities in China H Liu, K Jin, Z Duan Atmospheric Pollution Research 10 (5), 1588-1600, 2019 | 79 | 2019 |
| A novel ensemble model of different mother wavelets for wind speed multi-step forecasting H Liu, Z Duan, Y Li, H Lu Applied energy 228, 1783-1800, 2018 | 69 | 2018 |
| Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network H Liu, Z Duan, H Wu, Y Li, S Dong Measurement 148, 106971, 2019 | 65 | 2019 |
| Data multi-scale decomposition strategies for air pollution forecasting: A comprehensive review H Liu, S Yin, C Chen, Z Duan Journal of Cleaner Production 277, 124023, 2020 | 64 | 2020 |
| A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2. 5 concentrations H Liu, Z Duan, C Chen Information Sciences 516, 266-292, 2020 | 57 | 2020 |
| Wind speed forecasting using a new multi-factor fusion and multi-resolution ensemble model with real-time decomposition and adaptive error correction H Liu, R Yang, Z Duan Energy Conversion and Management 217, 112995, 2020 | 54 | 2020 |
| A new model using multiple feature clustering and neural networks for forecasting hourly PM2. 5 concentrations, and its applications in China H Liu, Z Long, Z Duan, H Shi Engineering 6 (8), 944-956, 2020 | 44 | 2020 |
| Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach R Yang, H Liu, N Nikitas, Z Duan, Y Li, Y Li Energy 239, 122128, 2022 | 39 | 2022 |
| A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model H Liu, Z Duan, C Chen, H Wu Energy conversion and management 199, 111975, 2019 | 33 | 2019 |
| A hybrid framework for forecasting PM2. 5 concentrations using multi-step deterministic and probabilistic strategy H Liu, Z Duan, C Chen Air Quality, Atmosphere & Health 12, 785-795, 2019 | 32 | 2019 |
| Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder H Liu, Z Duan, C Chen Applied Energy 280, 115975, 2020 | 31 | 2020 |
| Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation H Liu, Z Duan Energy conversion and management 203, 112265, 2020 | 31 | 2020 |
| An evolution-dependent multi-objective ensemble model of vanishing moment with adversarial auto-encoder for short-term wind speed forecasting in Xinjiang wind farm, China Z Duan, H Liu Energy conversion and management 198, 111914, 2019 | 28 | 2019 |
| A novel hybrid model for multi-step daily AQI forecasting driven by air pollution big data Y Xu, H Liu, Z Duan Air Quality, Atmosphere & Health 13, 197-207, 2020 | 27 | 2020 |
