副教授，硕士生导师

通讯地址：北京工商大学数学与统计学院，邮编：100048

电子信箱：20220940@btbu.edu.cn

办公室：良乡校区数统楼201室

个人简历

2013年6月毕业于西安财经大学，获学士学位

2016年6月毕业于兰州大学，获硕士学位

2020年6月毕业于东北财经大学，获博士学位

2022年9月至今，北京工商大学经济统计系任教

访问经历

2018年10月至2019年10月访问加拿大女皇大学

研究领域

主要研究大数据分析与挖掘、统计分析建模在能源环境系统的预测问题

主讲课程

非参数估计，机器学习

承担项目

1. 大规模风电并网过程中提升风能资源利用水平的若干对策与应用研究，国家自然科学基金青年科学基金项目，2022.1-2024.12，主持；

2. 基于非线性特征分析的风功率爬坡事件识别和概率预测研究，第69批中国博士后科学基金面上项目，2021.1-2023.12，主持；

3. 大数据时代雾霾污染经济损失评估及防治对策研究，国家社会科学基金重大项目，2018.1-2022.12，参与；

4. 基于集成学习的预测方法及其在全球大宗商品市场预测中的应用研究，国家自然科学基金面上项目，2023.1-2026.12，参与；

5. 基于多源大数据和分解集成方法论的旅游需求预测方法研究，国家自然科学基金面上项目，2023.1-2026.12，参与；

6. 基于多源大数据和分解集成方法论的旅游需求预测方法研究，国家自然科学基金面上项目，2023.1-2026.12，参与；

发表的主要论文

[1] Heng, J. , Hong, Y., Hu, J., & Wang, S* , Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information. Applied Energy, 306 (2022): 118029.

[2] Heng, J. , Wang, J*., Xiao, L., & Lu, H , Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting. Applied Energy, 208 (2017): 845-866.

[3] Heng, J. , Wang, C.*, Zhao, X., & Xiao, L. . Research and application based on adaptive boosting strategy and modified CGFPA algorithm: A case study for wind speed forecasting. Sustainability, 8(3) (2016): 235.

[4] Heng, J. , Wang, C.*, Zhao, X., & Wang, J , A hybrid forecasting model based on empirical mode decomposition and the cuckoo search algorithm: a case study for power load. Mathematical Problems in Engineering, (2016) .

[5] Hu, J., Heng, J. *, Wen, J., & Zhao, W , Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm. Renewable Energy, 162 (2020): 1208-1226.

[6] Hu, J., Heng, J. *., Tang, J., & Guo, M , 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.

[7] Wang, J., Heng, J. *, Xiao, L., & Wang, C , Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting. Energy, 125 (2017): 591-613.

[8] Du, Z., Heng, J. , Niu, M., & Sun, S* , An innovative ensemble learning air pollution early-warning system for China based on incremental extreme learning machine. Atmospheric Pollution Research, 12(9) (2021): 101153.

[9] Li, R., Hu, Y., Heng, J. , & Chen, X* , A novel multi-scale forecasting model for crude oil price time series. Technological Forecasting and Social Change, 173 (2021): 121181.

[10] Liu, T., Liu, S*., Heng, J. , & Gao, Y. A new hybrid approach for wind speed forecasting applying support vector machine with ensemble empirical mode decomposition and cuckoo search algorithm. Applied Sciences-Basel, 8(10) (2018): 1754.

[11] Jiang, P., Yang, H*. , & Heng, J.  , A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting. Applied Energy, 235 (2019): 786-801.

[12] Hu, J., Luo, Q., Tang, J., Heng, J. ., & Deng, Y , Conformalized temporal convolutional quantile regression networks for wind power interval forecasting. Energy 248 (2022), 123497;

[13] Tang, J., Hu, J., Heng, J. , & Liu, Z. (2022). A novel Bayesian ensembling model for wind power forecasting. Heliyon, 8(11) 2022, 11599.

社会兼职

[1] Data Science and Management期刊青年编委

[2] IEEE Transactions on Neural Networks and Learning Systems,Applied Energy和International Journal of Forecasting等SCI期刊匿名审稿人