Dr. Jianzhou Wang

Professor
Dongbei University of Finance and Economics, China

Highest Degree
Ph.D. in Probability and Statistics from Lanzhou Univeristy, China

My URL
https://livedna.org/86.9020

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Biography

Dr. Jianzhou Wang holds a position of Professor at School of Statistics, Dongbei University of Finance and Economics, China. He earned his Ph.D. in Probability and Statistics from Lanzhou University, China. Previously he worked at Gansu Grid, China, as a Power Load Management Engineer, and at Lanzhou University, China as Lecturer and Assistant Professor. He successfully completed 5 research projects as principal investigator. Dr. Jianzhou was received some awards such as Excellent Guide Teacher Award in Innovation and Entrepreneurship Action Plan, Excellent Guide Teacher Award in Undergraduate Mathematical Modeling Contest in China, and Guide students to win 188 prizes, as a Head Coach of Mathematical modeling in Lanzhou University since 2008. He is author and co-author of 63 journal papers.

Area of Interest:

Bayesian Inference
Data Processing
Data Analysis
Mathematical Modeling
Energy System Analysis

Selected Publications

  1. Xiao, L., J. Wang, Y. Dong and J. Wu, 2015. Combined forecasting models for wind energy forecasting: A case study in China. Renewable Sustainable Energy Rev., 44: 271-288.
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  2. Xiao, L., J. Wang, X. Yang and L. Xiao, 2015. A hybrid model based on data preprocessing for electrical power forecasting. Elect. Power Energy Syst., 64: 311-327.
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  3. Xiao, L., J. Wang, R. Hou and J. Wu, 2015. A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy, 82: 524-549.
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  4. Wang, J., S. Qin, Q. Zhou and H. Jiang, 2015. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renewable Energy, 76: 91-101.
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  5. Wang, J., S. Qin and S. Jin, 2015. Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources. Renewable Sustainable Energy Rev., 42: 26-42.
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  6. Wang, J., J. Hu, K. Ma and Y. Zhang, 2015. A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy, 78: 374-385.
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  7. Wang, J., H. Jiang, Y. Wu and Y. Dong, 2015. Forecasting solar radiation using an optimized hybrid model by cuckoo search algorithm. Energy, 81: 627-644.
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  8. Wang, J. and Y. Wang, 2015. The study and application of a novel hybrid forecasting model-A case study of wind speed forecasting in China. Applied Energy, 143: 472-488.
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  9. Lu, X., J. Wang and J. Zhao, 2015. Distributed HS-ARTMAP and Its forecasting model for electricity load. Applied Soft Computing, 32: 13-22.
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  10. Liu, L., Y. Wang, J. Wu, J. Wang and C. Xi, 2015. New optimized grey derivative models for grain production forecasting in China. J. Agric. Sci., 153: 257-269.
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  11. Hu, J., J. Wang and K. Ma, 2015. A hybrid technique for short-term wind speed prediction. Energy, 81: 563-574.
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  12. Zhou, Q., H. Jiang, J. Wang and J. Zhou, 2014. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci. Total Environ., 496: 264-274.
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  13. Zhao, W., J. Wang and H. Lu, 2014. Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model. Omega-Int. J. Manage. Sci., 45: 80-91.
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  14. Zhao, J., J. Wang and Z. Su, 2014. Power generation and renewable potential in China. Renewable Sustainable Energy Rev., 40: 727-740.
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  15. Wang, Z., F. Liu, J. Wu and J. Wang, 2014. A hybrid forecasting model based on bivariate division and a backpropagation artificial neural network optimized by chaos particle swarm optimization for day-ahead electricity price. Abstract Applied Anal., 10.1155/2014/249208.
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  16. Wang, J., Y. Dong and J. He, 2014. A study on the characteristics, predictions and policies of China's eight main power grids. Energy Conversion Manage., 86: 818-830.
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  17. Wang, J., W. Zhang, Y. Li, J. Wang and Z. Dang, 2014. Forecasting wind speed using empirical mode decomposition and Elman neural network. Applied Soft Comput., 23: 452-459.
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  18. Wang, J., W. Zhang, J. Wang, T. Han and L. Kong, 2014. A novel hybrid approach for wind speed prediction. Inform. Sci., 45: 80-91.
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  19. Wang, J., S. Jin, S. Qin and H. Jiang, 2014. Swarm intelligence-based hybrid models for short-term power load prediction. Math. Problems Eng., 10.1155/2014/712417.
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  20. Wang, J., Q. Zhou, H. Jiang and R. Hou, 2014. Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm. Math. Problems Eng., .
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  21. Wang, J., J. Wang, Y. Li, S. Zhu and J. Zhao, 2014. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. Int. J. Elect. Power Energy Syst., 62: 816-824.
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  22. Wang, J., H. Jiang, B. Han and Q. Zhou, 2014. An experimental investigation of FNN model for wind speed forecasting using EEMD and CS. Math. Problems Eng., 10.1155/2015/464153.
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  23. Wang, J. and S. Xiong, 2014. A hybrid forecasting model based on outlier detection and fuzzy time series-A case study on Hainan wind farm of China. Energy, 76: 526-541.
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  24. Wang, J. and L. Xiao, 2014. The combination forecasting of electricity price based on price spikes processing: A case study in South Australia. Abstract Applied Anal., 10.1155/2014/172306.
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  25. Su, Z., J. Wang, H. Lu and G. Zhao, 2014. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting. Energy Conversion Manage., 85: 443-452.
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  26. Ren, C., N. An, J. Wang, L. Li, B. Hu and D. Shang, 2014. Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowl.-Based Syst., 56: 226-239.
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  27. Qin, S., F. Liu, J. Wang and B. Sun, 2014. Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmospheric Environ., 98: 665-675.
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  28. Liu, L., Q. Wang, J. Wang and M. Liu, 2014. A rolling grey model optimized by particle swarm optimization in economic prediction. Computational Intell., 10.1111/coin.12059.
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  29. Liu, L., H. Zong, E. Zhao, C. Chen and J. Wang, 2014. Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development. Applied Energy, 124: 199-212.
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  30. Che, J. and J. Wang, 2014. Short-term load forecasting using a kernel-based support vector regression combination model. Applied Energy, 132: 602-609.
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  31. Zhang, W., J. Wang, X. Liu and J. Wang, 2013. Prediction of ozone concentration in semi-arid areas of china using a novel hybrid model. J. Environ. Informatics, 22: 68-77.
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  32. Zhang, W., J. Wang, J. Wang, Z. Zhao and M. Tian, 2013. Short-term wind speed forecasting based on a hybrid model. Applied Soft Comput., 13: 3225-3233.
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  33. Wu, J., J. Wang, H. Lu, Y. Dong and X. Lu, 2013. Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model. Energy Conversion Manage., 70: 1-9.
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  34. Wu, J., J. Wang and D. Chi, 2013. Wind energy potential assessment for the site of inner mongolia in China. Renewable Sustainable Energy Rev., 21: 215-228.
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  35. Ning, A., W. Zhao, J. Wang, D. Shang and E. Zhao, 2013. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy, 49: 279-288.
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  36. Hu, J., J. Wang and G. Zeng, 2013. A hybrid forecasting approach applied to wind speed time series. Renewable Energy, 60: 185-194.
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  37. Dong, Y., J. Wang, H. Jiang and X. Shi, 2013. Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China. Applied Energy, 109: 239-253.
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  38. Zhu, W., J. Wang, W. Zhang and D. Sun, 2012. Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling. Atmospheric Environ., 51: 29-38.
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  39. Zhao, Z., J. Wang, J. Zhao and Z. Su, 2012. Using a grey model optimized by differential evolution algorithm to forecast the per capita annual net income of rural households in China. Omega-Int. J. Manage. Sci., 40: 525-532.
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  40. Zhang, W., J. Wu, J. Wang, W. Zhao and L. Shen, 2012. Performance analysis of four modified approaches for wind speed forecasting. Applied Energy, 99: 324-333.
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  41. Wang, Y., J. Wang, G. Zhao and Y. Dong, 2012. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China. Energy Policy, 48: 284-294.
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  42. Wang, J., X. Ma, J. Wu and Y. Dong, 2012. Optimization models based on GM (1, 1) and seasonal fluctuation for electricity demand forecasting. Int. J. Elect. Power Energy Syst., 43: 109-117.
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  43. Wang, J., R. Jia, W. Zhao, J. Wu and Y. Dong, 2012. Application of the largest lyapunov exponent and non-linear fractal extrapolation algorithm to short-term load forecasting. Chaos Solitons Fractals, 45: 1277-1287.
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  44. Wang, J., J. Wang, Z. Zhang and S. Guo, 2012. Stock index forecasting based on a hybrid model. Omega-Int. J. Manage. Sci., 40: 758-766.
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  45. Wang, J., H. Lu, Y. Dong and D. Chi, 2012. The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term. Applied Math. Model., 36: 1184-1196.
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  46. Guo, Z., W. Zhao, H. Lu and J. Wang, 2012. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renewable Energy, 37: 241-249.
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  47. Che, J., J. Wang and Y. Tang, 2012. Optimal training subset in a support vector regression electric load forecasting model. Applied Soft Comput., 12: 1523-1531.
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  48. Che, J., J. Wang and G. Wang, 2012. An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting. Energy, 37: 657-664.
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  49. Zhu, S., J. Wang, W. Zhao and J. Wang, 2011. A seasonal hybrid procedure for electricity demand forecasting in China. Applied Energy, 88: 3807-3815.
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  50. Wang, J.Z., J.J. Wang, Z.G. Zhang and S.P. Guo, 2011. Forecasting stock indices with back propagation neural network. Exp. Syst. Applic., 38: 14346-14355.
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  51. Wang, J., Y. Dong, J. Wu, R. Mu and H. Jiang, 2011. Coal production forecast and low carbon policies in China. Energy Policy, 39: 5970-5979.
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  52. Wang, J., S. Zhu, W. Zhao and W. Zhu, 2011. Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm. Expert Syst. Appli., 38: 8151-8158.
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  53. Wang, J., D. Chi, J. Wu and H. Lu, 2011. Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting. Expert Syst. Appli., 38: 8419-8429.
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  54. Guo, Z., J. Zhao, W. Zhang and J. Wang, 2011. A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. Energy, 36: 1668-1679.
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  55. Guo, Z., J. Wu, H. Lu and J. Wang, 2011. A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Syst., 24: 1048-1056.
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  56. Dong, Y., J. Wang, H. Jiang and J. Wu, 2011. Short-term electricity price forecast based on the improved hybrid model. Energy Conversion Manage., 52: 2987-2995.
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  57. Cai, Y., J. Wang, Y. Tang and Y. Yang, 2011. An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network) neural network. Energy, 36: 1340-1350.
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  58. Wang, J., S. Zhu, W. Zhang and H. Lu, 2010. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy, 35: 1671-1678.
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  59. Guo, Z., Y. Dong, J. Wang and H. Lu, 2010. The forecasting procedure for long-term wind speed in the zhangye area. Math. Problems Eng., 10.1155/2010/684742.
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  60. Che, J. and J. Wang, 2010. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling. Energy Conversion Manage., 51: 1911-1917.
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  61. Wang, J., W. Zhu, W. Zhang and D. Sun, 2009. A trend fixed on firstly and seasonal adjustment model combined with the [epsilon]-SVR for short-term forecasting of electricity demand. Energy Policy, 37: 4901-4909.
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  62. Wang, J., Z. Ma and L. Li, 2005. Mining and forecasting of impact load in power load forecasting. Apply Math. Computation, 168: 29-39.
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