| Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction Y Li, H Wu, H Liu Energy Conversion and Management 167, 203-219, 2018 | 157 | 2018 |
| 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 | 154 | 2020 |
| Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction H Liu, H Wu, Y Li Energy Conversion and Management 161, 266-283, 2018 | 116 | 2018 |
| An intelligent hybrid model for air pollutant concentrations forecasting: Case of Beijing in China H Liu, H Wu, X Lv, Z Ren, M Liu, Y Li, H Shi Sustainable Cities and Society 47, 101471, 2019 | 74 | 2019 |
| 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 |
| A hybrid model for appliance classification based on time series features H Liu, H Wu, C Yu Energy and Buildings 196, 112-123, 2019 | 59 | 2019 |
| A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network H Liu, C Yu, C Yu, C Chen, H Wu Advanced Engineering Informatics 44, 101089, 2020 | 39 | 2020 |
| 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 | 34 | 2019 |
| Multi-step wind speed forecasting model based on wavelet matching analysis and hybrid optimization framework H Liu, H Wu, Y Li Sustainable Energy Technologies and Assessments 40, 100745, 2020 | 30 | 2020 |
| A hybrid neural network model for marine dissolved oxygen concentrations time-series forecasting based on multi-factor analysis and a multi-model ensemble H Liu, R Yang, Z Duan, H Wu Engineering 7 (12), 1751-1765, 2021 | 26 | 2021 |
| An improved non-intrusive load disaggregation algorithm and its application H Liu, C Yu, H Wu, C Chen, Z Wang Sustainable cities and society 53, 101918, 2020 | 25 | 2020 |
| Non-intrusive load transient identification based on multivariate LSTM neural network and time series data augmentation H Wu, H Liu Sustainable Energy, Grids and Networks 27, 100490, 2021 | 23 | 2021 |
| PM2. 5 concentrations forecasting using a new multi-objective feature selection and ensemble framework H Wu, H Liu, Z Duan Atmospheric Pollution Research 11 (7), 1187-1198, 2020 | 22 | 2020 |
| Smart Device Recognition: Ubiquitous Electric Internet of Things H Liu, C Yu, H Wu Springer Nature, 2020 | 5 | 2020 |
| Smart non-intrusive device recognition based on deep learning methods H Liu, C Yu, H Wu, H Liu, C Yu, H Wu Smart Device Recognition: Ubiquitous Electric Internet of Things, 229-258, 2021 | 2 | 2021 |
| Potential Applications of Smart Device Recognition in Industry H Liu, C Yu, H Wu, H Liu, C Yu, H Wu Smart Device Recognition: Ubiquitous Electric Internet of Things, 259-294, 2021 | 2 | 2021 |
| BLE Beacon-based floor detection for mobile robots in a multi-floor automation Laboratory H Wu, H Liu, T Roddelkopf, K Thurow Transportation Safety and Environment 6 (2), tdad024, 2024 | 1 | 2024 |
| Household Appliance Identification Based on a Novel Load Signature Processing Framework Z Ren, B Tang, L Wang, H Liu, S Dong, H Wu 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration …, 2019 | 1 | 2019 |
| BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory H Wu, S Junginger, T Roddelkopf, H Liu, K Thurow Transportation Safety and Environment, tdad033, 2023 | | 2023 |
| Smart Non-intrusive Device Recognition Based on Intelligent Clustering Methods H Liu, C Yu, H Wu, H Liu, C Yu, H Wu Smart Device Recognition: Ubiquitous Electric Internet of Things, 143-167, 2021 | | 2021 |
