胡建明，廣州大學經濟與統計學院教師，理學博士，副教授，2017年廣州大學“青年學術新銳”獲得者。從事統計學習方法及其套用方面的研究，包括核學習方法與理論，貝葉斯學習，統計計算和隨機最佳化算法等，並主要針對風電場的實時發電問題進行研究。在國際期刊Renewable&Sustainable Energy Reviews，Energy和Renewable Energy等雜誌發表SCI論文9篇，其中JCR一區論文7篇，一篇入選當年度熱點論文，並被廣泛引用。目前主持國家青年基金項目1項。
基本介紹
- 中文名：胡建明
- 學位/學歷：博士
- 專業方向：貝葉斯學習、套用統計、正則化學習
- 職稱：副教授
研究方向,教學方向,科研項目,發表論文,
研究方向
1.貝葉斯學習
2.套用統計
3.正則化學習
教學方向
1.複雜數據分析
2.統計計算
科研項目
1.主持國家自然科學基金項目: “含爬坡事件的風電功率的機率預測及電池儲能系統容量預測控制的方法研究”(71701053),2018/1-2020/12.
發表論文
[1].Jianming Hu, Jianzhou Wang, Guowei Zeng. A hybrid forecasting approach applied to wind speed time series. Renewable Energy 60 (2013) 185-94. (SCI 一區)( 該論文被美國 ISI Web of science 基本科學指標 ESI列為領域學科熱點論文
[2].Jianming Hu, Jianzhou Wang, Kailiang Ma. A hybrid technique for short-term wind speed prediction.Energy81(2015) 563-574.(SCI 一區)
[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 一區)
[4]. Jianming Hu, Jianzhou Wang. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 93(2015)1456-1466.(SCI 一區)
[5]. Jianzhou Wang, Jianming Hu, Kailiang Ma. A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy 78(2015) 374-385. (SCI 一區)
[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一區)
[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一區)
[8] SulingZhu，XiuyuanLian，HaixiaLiu，JianmingHu. Daily air quality index forecasting with hybrid models: A case in China; Environmental Pollution; 231(2017),1232-1244(SCI二區)
[9].Jianming Hu, Jianzhou Wang. Forecasting day-ahead electricity price using a hybrid improved approach. Journal of Electrical Engineering & Technology; 12(2017): 2166-2176
