胡建明      

Email：hujm17@gzhu.edu.cn

研究兴趣与方向：

数据挖掘、大数据与人工智能、应用统计理论与方法，新能源并网与电力系统分析，机器学习，复杂网络，组合优化，风险与决策，预测与决策/控制等

个人简介

胡建明，理学博士(与加拿大女王大学联合培养），广州大学经济与统计学院教授，博士后导师。在《Renewable & Sustainable Energy Reviews》、《Information Sciences》、《Energy Conversion and Management》和《Applied Soft Computing》等国际期刊发表SCI论文20余篇（一作或通讯作者 SCI 一区 16 篇），发表论文的总被为引次数超1300次，两篇入选ESI高被引论文。主持或完成国家自然科学基金项目2项，广东省自然科学基金面上项目1项，市校级项目2项。

教育背景

2013.09至2016.12，兰州大学，概率论与数理统计       博士

2011.09至2013.6，  兰州大学， 应用统计                      硕士

2003.09至2007.6，  郑州大学，工业工程专业               本科

职业经历

1.学术工作经历

2017.06至今，广州大学经济与统计学院统计系，教师/副教授、教授

2.海外经历

2015.08至2016.09，加拿大皇后大学，访问学者/联合培养博士

教授课程

本科生：数据挖掘，统计学，非参数统计,自然语言处理，金融计算与建模，运筹学

研究生：数据挖掘，复杂数据分析，统计计算

科研项目

国家自然科学基金面上项目，主持，基于时空数据的风电功率协同概率预测研究：立项时间：2020年，在研。

国家自然科学基金青年项目，主持，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：立项时间：2017年，结项。

广东省自然科学基金面上项目，主持，风能资源时空变异性评估与含时空信息的风电功率区间预测的研究，立项时间：2020年，结项。

市校（院）联合资助项目，时空分位数深度模型的研究及其在风电功率预测的应用，2021年，在研。

学校科研启动项目，时空分位数模型的开发及其在风电功率多步预测应用研究，2017年，结项。

部分研究成果

Hu Jianming, Tang J, Liu Z. A novel time series probabilistic prediction approach based on the monotone quantile regression neural network[J]. Information Sciences, 2023: 119844.(中国科学院分区表1区Top期刊)

Zhan H, Zhu X, Qiao Z, Hu Jianming*. Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions[J]. Analytica Chimica Acta, 2023, 1244: 340558.(中国科学院分区表1区Top期刊)

Zhan H, Zhu X, Hu Jianming*. A probabilistic forecasting approach for air quality spatio-temporal data based on kernel learning method[J]. Applied Soft Computing, 2023, 132: 109858..(中国科学院分区表1区Top期刊)

Hu Jianming, Zhang L, Tang J, et al. A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting[J]. Energy, 2023: 128075..(中国科学院分区表1区Top期刊)

Hu, Jianming, et al. Conformalized temporal convolutional quantile regression networks for wind power interval forecasting. Energy 248 (2022): 123497. (1)(中国科学院分区表1区Top期刊)

Jiani Heng,Yongmiao Hong, Jianming Hu, Shouyang Wang; Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information. Applied Energy,306(2022) 118029.(5)(中国科学院分区表1区Top期刊)

Jianming Hu, YingyingLin, JingweiTang, JingZhao. A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function. Applied Soft Computing 92 (2020): 106327.(中国科学院分区表1区Top期刊 )

Jianming Hu, Weigang Zhao, Jingwei Tang, Qingxi Luo. Integrating a softened multi-interval loss function into neural networks for wind power prediction. Applied Soft Computing, 113 (2021) 108009.(中国科学院分区表1区Top期刊 )

Jianming Hu, Jiani Heng, JiemeiWen,Weigang Zhao. Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm. Renewable Energy 162 (2020): 1208-1226.(中国科学院分区表1区Top期刊 )

Jianming Hu, Jingwei Tang, Yingying Lin. A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization. Renewable Energy, 149 (2020): 141-164.(中国科学院分区表1区Top期刊 )

Jing Zhao, Zhenhai Guo, Yanling Guo, Ye Zhang, Wantao Lin, Jianming Hu. Wind resource assessment based on numerical simulations and an optimized ensemble system. Energy Conversion and Management ; Energy Conversion and Management 201 (2019): 112164.(中国科学院分区表1区Top期刊 )

Jianming Hu, Jianni Heng, Jingwei Tang, et al. Research and application of a hybrid model based on Meta learning strategy for wind power deterministic and probabilistic forecasting. Energy Conversion and Management ;173 (2018): 197-209.(中国科学院分区表1区Top期刊)

Chengshi Tian, Yan Hao, Jianming Hu. A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Applied Energy; 231 (2018): 301-319.(中国科学院分区表1区Top期刊 )

Jianming Hu，Jianzhou Wang，Liqun Xiao. A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts. Renewable Energy. 114(2017): 670-685.(中国科学院分区表1区Top期刊 )

Suling Zhu，Xiuyuan Lian，Haixia Liu，Jianming Hu. Daily air quality index forecasting with hybrid models: A case in China; Environmental Pollution; 231(2017):1232-1244.(中国科学院分区表1区Top期刊 )

Jianzhou Wang, Jianming Hu*, Kailiang Ma. Wind speed probability  distribution estimation and wind energy assessment. Renewable and sustainable energy reviews; 60(2016):881-899.（*代表通讯作者）(中国科学院分区表1区Top期刊 )

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. Energy. 93(2015):41-56.（*代表通讯作者）(中国科学院分区表1区Top期刊 )

Jianming Hu, Jianzhou Wang, Kailiang Ma. A hybrid technique for short-term wind speed prediction. Energy; 81(2015): 563-574.(中国科学院分区表1区Top期刊 )

Jianzhou Wang, Jianming Hu*, Kailiang Ma, Yixin Zhang. A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy. Renewable Energy 78 (2015): 374-385.（*代表通讯作者）(中国科学院分区表1区Top期刊 )

Jianming Hu, Jianzhou Wang. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy;93(2015) :1456-1466.(中国科学院分区表1区Top期刊)

Jianming Hu, Jianzhou Wang, Guowei Zeng. A hybrid forecasting approach applied to wind speed time series. Renewable Energy; 60 (2013):185-94.(中国科学院分区表1区Top期刊 )

社会服务

1、期刊《Data Science and Management》的青年编委

2、《Applied Energy》、《IEEE Transactions on Industrial Electronics》等SCI 期刊匿名审稿人；中国“双法”研究会能源经济与管理研究分会理事会，中国青年统计家协会理事，广东省现场统计协会常务理事。