胡建明副教授



通讯地址：广州大学文清楼613室
系 所：统计系

个人简历
经济与统计学院教师，理学博士，副教授，2017年广州大学“青年学术新锐”获得者。从事统计学习方法及其应用方面的研究，包括核学习方法与理论，贝叶斯学习，统计计算和随机优化算法等，并主要针对风电场的实时发电问题进行研究。在国际期刊Renewable&Sustainable Energy Reviews，Energy和Renewable Energy等杂志发表SCI论文9篇，其中JCR一区论文7篇，一篇入选当年度热点论文，并被广泛引用。目前主持国家青年基金项目1项。

研究方向
1.贝叶斯学习
2.应用统计
3.正则化学习

教学方向
1.复杂数据分析
2.统计计算

科研项目
1.主持国家自然科学基金项目: “含爬坡事件的风电功率的概率预测及电池储能系统容量预测控制的方法研究”(**),2018/1-2020/12.

发表论文
[1].Jianming Hu, Jianzhou Wang, Guowei Zeng. A hybrid forecasting approach applied to wind speed time series. Renewable Energy 60 (2013) 185-94. (SCI 一区)( 该论文被美国 ISI Web of science 基本科学指标 ESI列为领域学科热点论文
[2].Jianming Hu, Jianzhou Wang, Kailiang Ma. A hybrid technique for short-term wind speed prediction.Energy81(2015) 563-574.(SCI 一区)
[3].Jianzhou Wang, Jianming Hu. A robust combination approach for short-term wind speed forecasting and analysis--Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine),SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model. 93(2015) 41-56. (SCI 一区)
[4]. Jianming Hu, Jianzhou Wang. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 93(2015)1456-1466.(SCI 一区)
[5]. Jianzhou Wang, Jianming Hu, Kailiang Ma. A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy 78(2015) 374-385. (SCI 一区)
[6].Jianzhou Wang, Jianming Hu, Kailiang Ma. Wind speed probability distribution estimation and wind energy assessment.renewable and sustainable energy reviews 60(2016)881-899(SCI一区)
[7]. Jianzhou Wang, Jianming Hu. A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts. Renewable Energy,114(2017),670-685(SCI一区)
[8] SulingZhu，XiuyuanLian，HaixiaLiu，JianmingHu. Daily air quality index forecasting with hybrid models: A case in China; Environmental Pollution; 231(2017),1232-1244(SCI二区)
[9].Jianming Hu, Jianzhou Wang. Forecasting day-ahead electricity price using a hybrid improved approach. Journal of Electrical Engineering & Technology; 12(2017): 2166-2176