1.He Jiang*,Qiang Liu. Graphical regularized quantile regression with Reinforcement Learning. Journal of Forecasting.2026, Accepted

2. He Jiang, Sheng Pan*,Jiaxuan Yue, Jianzhou Wang, Yawei Dong. Reinforcement Learning-Driven Probabilistic Price Forecasting for Energy Inventory Arbitrage. Transportation Research Part E: Logistic and Transportation Review.2026,212,104956. (FMS B类，ABS 三星)

3. He Jiang, Rongyu Cao,Xue-li Chen, Malin Song*, Juntao Du. Predictability and Causal Identification of Carbon Prices using Interpretable Variables: Evidence from China’s Hubei Carbon Market. Energy Economics 2026, 158,109330. (FMS B类，ABS 三星)

4. Yawei Dong,He Jiang*,Bo Zeng,Sheng Pan, Reinforcement learning driven periodic kernel fusion for probabilistic forecasting of market dynamics. Knowledge-Based Systems 2026,340,115770. （通讯作者）

5. He Jiang,Yawei Dong*. Integrating Uncertainty in Electricity Price Forecasting: A Probabilistic Model with Dynamic Trading Applications. Applied Economics, 2026,Accepted.

6. 董亚伟，江河*，曹戎彧. 基于贝叶斯深度学习的碳价分布建模与不确定性量化，能源与气候变化，2026， 10.3724/j.issn.2097-4981.JECC-2025-0380.

7. He Jiang, Yawei Dong*, Shaolong Sun, Shouyang Wang. How Uncertain are Oil Prices? Bayesian Regularized Distribution Neural Network for Forecasting Oil Prices. Applied Economics, 2026,58（9），1639-1655. 

8.Yao Dong, Kai Liu*, He Jiang, Yawei Dong, Jianzhou Wang, Power load forecasting using deep learning and reinforcement learning. Information    Sciences. 2025,  122523. （FMS B类，CCF B类）

9. Yao Dong, He Jiang*, Sheng Pan, Jianzhou Wang. Graph-constrained quantile regression: unifying structured regularization and robust modeling for  enhanced accuracy  and interpretability. Information Sciences. 2025, 122530（通讯作者）.(FMS B类, CCF B类).

10. He Jiang, Yawei Dong*, Shaolong Sun, Shouyang Wang. How Uncertain are Oil Prices? Bayesian Regularized Distribution Neural Network for Forecasting Oil Prices. Applied Economics, 2025, http://doi.org/10.1080/00036846.2025.2468937 (FMS C类，ABS 二星）

11. He Jiang*, Xuxilu Zhang, Yao Dong, Jianzhou Wang. Data-Driven Predictive Modeling of Citywide Crowd Flow for Urban Safety Management. Journal of Forecasting. 2025, 44（2），730-752.. (FMS C类, ABS二星).

12. He Jiang, Yawei Dong*,Yao Dong, Jianzhou Wang. Probabilistic electricity price forecasting by integrating interpretable model. Technological Forecasting and Social Change. 2025, 210,123846. （FMS B类，ABS 三星， IF：12.0）

13. He Jiang, Yawei Dong*,Yao Dong, Jianzhou Wang. Power load forecasting based on spatial-temporal fusion graph convolution network . Technological Forecasting and Social Change. 2024, 204,123435. （FMS B类，ABS 三星， IF：12.0）

14. He Jiang, Ye Yang*, Qiuying Wan, Yao Dong, Feature selection based on Dynamic Crow Search Algorithm for High-dimensional Data Classification. Expert System with Applications. 2024, 123871. (SCI一区，IF : 8.5 top期刊).

15. Shaolong Sun, Yawei Dong, He Jiang*, Shouyang Wang. A novel Multistep Ahead PM2.5 Forecasting Approach using Spatial-Temporal Attention Network. IEEE Transactions on Industrial Informatics. 2024.20（7）, 9761-9770（通讯作者）(SCI一区, IF:12.3, top期刊).

16. Yao Dong, He Jiang*, Yunting Guo, Jianzhou Wang. A novel crude oil price forecasting model using decomposition and deep learning networks. Engineering Applications of Artificial Intelligence. 2024,108111. （通讯作者）(SCI 一区, IF:8.0, top 期刊).

17. He Jiang*, Yao Dong, Jianzhou Wang, Electricity price forecasting using quantile regression averaging with nonconvex regularization. Journal of Forecasting, 2024. 43（6）,1859-1879（通讯作者）(FMS C类, ABS二星).

18. He Jiang*, Sheng Pan, Yao Dong, Jianzhou Wang. Probabilistic electricity price forecasting based on penalized temporal fusion transformer. Journal of Forecasting, 2024. 43（5）, 1465-1491.（通讯作者）(FMS C类, ABS二星).

19. He Jiang*. Forecasting global solar radiation using a robust regularization approach with mixture kernels. Journal of Forecasting, 2023, 42（8）,1989-2010. （通讯作者）(FMS C类, ABS二星).

20. He Jiang*. Robust forecasting in spatial autoregressive model with total variation regularization. Journal of Forecasting, 2023, 43（2）, 195-211. 
（通讯作者）(FMS C类, ABS二星).

21. He Jiang, Weiqiang Hu*, Ling Xiao, Yao Dong, A decomposition ensemble based deep learning approach for crude oil price forecasting, Resources Policy,78,2022, 102855. (FMS C类, ABS 二星).

22. He Jiang*. A novel robust structural quadratic forecasting model and applications. Journal of Forecasting, 2022, 41（6）,1156-1180. （通讯作者） (FMS C类, ABS二星).

23. He Jiang, Weihua Zheng*, Deep learning with regularized robust long and short-term memory network for probabilistic short-term load forecasting. Journal of Forecasting, 2022, 41（6）,1047-1313. (FMS C类, ABS二星).

24. He Jiang*, Weihua Zheng, Yao Dong. Sparse and robust estimation with ridge minimax concave penalty. Information Sciences, 2021;571: 154-174（通讯作者） (FMS B类，CCF B类，JCR Q1，top期刊).

25. He Jiang, Changqi Tao*, Yao Dong, Ren Xiong. Robust Low-rank Multiple Kernel Learning with Compound Regularization. European Journal of Operational Research, 2021;295（2），634-647. (FMS A类，ABS四星，管理学top期刊).

26. He Jiang, Shihua Luo*, Yao Dong. Simultaneous feature selection and clustering based on square root optimization. European Journal of Operational Research, 2021; 289（1）: 214-231. (FMS A类，ABS四星，管理学top期刊).

27. Yiyuan She*, Zhifeng Wang, He Jiang. Group regularized estimation under structural hierarchy. Journal of the American Statistical Association (JASA). 2018; 113 (521); 445-454. (FMS A类，统计学 top期刊, IF:2.123)

28. He Jiang, Weihua Zheng*, Liangqing Luo, Yao Dong, A two-stage minimax concave penalty based method in pruned Adaboost ensemble. Applied Soft Computing 2019; 83: 105674; (JCR Q1, IF: 8.263)

29. He Jiang*, Yao Dong, Structural regularization in quadratic logistic regression model. Knowledge-Based Systems 2019; 163: 842-857.（通讯作者）. (FMS C类，JCR Q1, IF: 5.101)

30. He Jiang, Sparse estimation based on square root nonconvex optimization in high dimensional data. Neurocomputing 2018;282: 122-135. (JCR Q1, IF: 5.579)

31. He Jiang, Model forecasting based on two-stage feature selection procedure using orthogonal greedy algorithm. Applied Soft Computing 2018; 63: 110-123. (JCR Q1, IF: 8.263)

32. He Jiang*, Yao Dong; Dimension reduction based on a penalized kernel support vector machine model. Knowledge-Based Systems 2017; 138:79-90 （通讯作者）. (FMS C类，JCR Q1, IF: 8.139)

33. He Jiang, A novel approach for forecasting global horizontal irradiance based on sparse quadratic RBF neural network. Energy Conversion and Management 2017;152:266-280. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 11.533, top期刊)

34. He Jiang, Yao Dong*, Forecast of hourly global horizontal irradiance based on Structured Kernel Support Vector Machine: A case study of Tibet area in China. Energy Conversion and Management 2017;142:307-321. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 11.533, top期刊)

35. He Jiang, Yao Dong*, Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China. Energy 2017;133:270-283. (能源电力领域高质量科技期刊T1，JCR Q1, IF:5.537, top期刊)

36. He Jiang, Yao Dong*, Ling Xiao. A multi-stage intelligent approach based on an ensemble of two-way Interaction Model for forecasting the global horizontal radiation of India. Energy Conversion and Management 2017;137:142-154. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 11.533, top期刊)

37. He Jiang, Yao Dong*. A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation. Energy Conversion and Management 2016;126:991-1002. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 11.533, top期刊)

38. He Jiang, Jianzhou Wang*, Yao Dong, Haiyan Lu. Comprehensive assessment of wind resources and low-carbon economy policies: An empirical study in Alxa and Xilin Gol League of Inner Mongolia, China. Renewable & Sustainable Energy Reviews 2015;50:1304-1319. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 16.799, top期刊)

39. He Jiang, Yao Dong, Jianzhou Wang, Quqin Li. Haiyan Lu. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Conversion and Management 2015;95:42-58. (能源电力领域高质量科技期刊T1，JCR Q1, IF: 11.533, top期刊)

 主持课题 

1. 国家自然科学基金面上项目，72371196，适应电力负荷特征挖掘的稳健概率预测与多维度评价体系研究，41万元，2024/01-2027/12，在研.
2. 西安交通大学青年拔尖人才支持计划A类，50万元，2021/04-2027/04，在研.
3. 国家自然科学基金青年项目，71901109，基于时空相关性的光伏功率稳健概率预测与评价体系研究，19万元，2020/01-2022/12, 已结题. 结题绩效评估为 优
4. 国家自然科学基金地区项目，71861012，光伏并网发电系统中的短期功率预测与储能容量优化配置研究，29万元，2019/01-2022/12， 已结题. 结题绩效评估为 优
5. 西安交通大学2024年度第二批基本科研业务费（人文社科类）重大成果专项，基于特征选择与智能优化的光伏发电与预测理论与方法研究，5万元，2024/07-2025/01，已结题.
6. 西安交通大学2024年本科教学改革研究青年项目，基于“人工智能+”的混合式教学模式改革与实践-以《经济预测与决策》课程为例，0.5万元，2024/07-2025/05，已结题.
7. 江西省“双千计划”科技创新高端人才项目，基于不确定性特征挖掘的光伏功率在线概率预测研究， 100万元，2020/01-2022/12, 已结题.
8. 江西省杰出青年基金项目，20212ACB211003，适应光伏功率不确定性的在线概率预测研究， 20万元，2021/01-2023/12, 已结题.
9. 第11批中国博士后特别资助，2018T110654，基于特征选取的短期光伏功率预测与储能容量优化研究，15万元，2019/01-2020/12，已结题.
10. 第62批中国博士后科学基金面上资助一等资助，2017M620277，适应复杂天气的光伏发电短期功率预测研究，8万元，2018/01-2019/12，已结题.
11. 江西省自然科学基金青年项目, 20181BAB211020, 适应复杂天气的光伏发电短期功率预测与影响因素研究, 6万元, 2018/01-2020/12, 已结题
12. 江西财经大学第九批优秀青年学术人才支持计划, 光伏并网发电系统中的短期功率预测与影响因素研究，6万元，2019/01-2021/12，已结题.
13. 江西省博士后研究人员科研项目择优资助一等资助，2018KY08，能源互联网背景下光伏发电短期功率预测与储能容量优化研究，8万元，2019/01-2020/12，已结题
14. 江西省博士后研究人员日常经费资助，2017RC38，基于特征选取的光伏发电短期功率预测研究，3万元，2018/01-2019/12，已结题.
15. 江西省教育厅科学技术研究项目，GJJ160454，自适应特征提取方法在光伏功率预测模型中的应用研究，2万元，2017/01-2018/12，已结题.
16. 江西省教育厅科学技术研究项目，GJJ190264，考虑复杂不确定性特征的光伏功率在线概率预测研究，3万元，2020/01-2021/12，已结题.
17. 江西省高校人文社会科学研究项目，TJ19202，能源互联网下光伏发电短期功率预测研究，1万元，2020/01-2021/12, 已结题.
18. 江西财经大学2017博士后项目，基于特征选取的光伏发电短期功率预测研究，1万元，2018/01-2019/12，已结题.
19. 江西财经大学海归博士人才项目，10万元，2015/10-2018/10，已结题.

参与课题

1. 国家社会科学基金重大项目， 21&ZD152， 《工业大数据统计测度理论及应用研究》，2021年立项，80万元，已结题.
2. 国家重点研发计划， 2022YFF0903000，《现代服务业发展水平评价理论与方法研究》，200万元，2022/11-2025/10，在研(项目骨干成员，第一参与人）.
3. 国家自然科学基金地区项目，71761016，风光互补并网发电系统中的资源评估与短期功率预测研究，27万元，2018/01-2021/12，已结题.
4. 江西省自然科学基金青年项目，20171BAA218001，基于数据挖掘的动态自适应风速预测及风能资源评估研究，6万元，2017/01-2019/12，已结题.
5. 国家自然科学基金面上项目，41271038，无资料地区水文数据反演和数学建模，75万元，2013/01-2016/12，已结题
6. 云南省2019年贫困县退出专项评估检查项目，横20200164, 321万元，2020/04-2020/09，已结题

学术专著

      《基于特征选择与智能优化的光伏发电预测理论与方法研究》，独著，西安交通大学出版社，2025，8.  ISBN:978-7-5693-4125-6