Cross-disciplinary research integrating NWP and AI, Wind energy, Forecasting techniques. 

• 2014.4 – 2016.6: Institute of Atmospheric Physics, Chinese Academy of Sciences, Visiting Student
• 2013.9 – 2016.6: School of Mathematics and Statistics, Lanzhou University, Ph.D.
• 2010.9 – 2013.6: School of Mathematics and Statistics, Lanzhou University, Master's
• 2006.9 – 2010.6: School of Mathematics and Statistics, Lanzhou University, Bachelor's

• 2021.8 – Present: Associate Researcher, Institute of Atmospheric Physics, Chinese Academy of Sciences
• 2016.7 – 2021.6: Postdoctoral Researcher, Institute of Atmospheric Physics, Chinese Academy of Sciences

[1] A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling. ENERGY 2024;302:131787. [First Author]

[2] Multistep prediction of wind speed based on grey wolf algorithm and extreme learning machine. JOURNAL OF ZHENGZHOU UNIVERSITY (ENGINEERING SCIENCE) 2024;45:89–96. [Corresponding Author]

[3] Ultra-short-term wind speed forecasting based on optimal combination model of secondary decomposition and crow search algorithm. ACTA ENERGIAE SOLARIS SINICA 2024;45:73–82. [Corresponding Author]

[4] Combined wind speed forecasting model based on secondary decomposition and quantile regression closed-form continuous-time neural network. INTERNATIONAL JOURNAL OF GREEN ENERGY 2023;21:1793–814. [Corresponding Author]

[5] A hybrid forecasting model for very short-term wind speed prediction based on secondary decomposition and deep learning algorithms. EARTH SCIENCE INFORMATICS 2023;16:2421–38.

[6] A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions. ENERGY 2021;218:119509. [First Author]

[7] Multi-step wind speed forecast based on sample clustering and an optimized hybrid system. RENEWABLE ENERGY 2021;165:595–611. [Corresponding Author]

[8] A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function. APPLIED SOFT COMPUTING 2020;92:106327.

[9] Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method. APPLIED ENERGY 2019;255:113833. [First Author]

[10] Wind resource assessment based on numerical simulations and an optimized ensemble system. ENERGY CONVERSION AND MANAGEMENT 2019;201:112164. [First Author]

[11] Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the cuckoo search algorithm. ATMOSPHERIC AND OCEANIC SCIENCE LETTERS 2019;12:107–15. [Corresponding Author]

[12] Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. APPLIED ENERGY 2017;197:183–202. [First Author]

[13] A Study on the Identification and Synoptic Analysis of Wind Ramp Events in Shandong Peninsula. CLIMATIC AND ENVIRONMENTAL RESEARCH 2017;22:97–107. [Corresponding Author]

[14] Multistep forecasting for short-term wind speed using an optimized extreme learning machine network with decomposition-based signal filtering. JOURNAL OF ENERGY ENGINEERING 2016;142:4015036. [First Author]

[15] An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. APPLIED ENERGY 2016;162:808–26. [First Author]

[16] Hybrid wind speed prediction based on a self-adaptive ARIMAX model with an exogenous WRF simulation. ENERGIES 2016;9:7. [Corresponding Author]

[17] A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. APPLIED SOFT COMPUTING 2016;48:281–97.

[18] Distributed HS-ARTMAP and its forecasting model for electricity load. APPLIED SOFT COMPUTING 2015;32:13–22.

[19] Power generation and renewable potential in China. RENEWABLE AND SUSTAINABLE ENERGY REVIEWS 2014;40:727–40. [First Author]

[20] Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014;62:816–24.

[21] Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method. ABSTRACT AND APPLIED ANALYSIS 2014:984268. [Corresponding Author]

[22] Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2012;40:525–32.

[23] A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. ENERGY 2011;36:1668–79. [Corresponding Author]