邮箱：20220940@btbu.edu.cn

地址：北京市房山区qmh球盟会良乡主校区东区球盟会201

个人简介

球盟会副教授，硕士生导师。中国科公司数学与系统科学研究院博士后，东北财经大学与加拿大女皇大学联合培养博士。兼任Data Science and Management期刊青年编委。IEEE Transactions on Neural Networks and Learning Systems、Applied Energy、International Journal of Forecasting等SCI期刊匿名审稿人。

研究兴趣

主要从事预测理论与方法、能源经济分析、机器学习与大数据分析等方面的研究。现已在Nature Communications、IEEE Transactions on Power Systems、Applied Energy、Energy、Knowledge-Based Systems等发表SCI/SSCI学术论文20余篇，其中2篇入选ESI全球前1%TOP高被引论文。欢迎具有数学、统计学、信息科学、经济学、管理科学与工程等相关背景的同学加入。

主讲课程

本科生课程《非参数统计学》、《计量经济学》、《金融计量学》

研究生课程《机器学习》、《统计案例分析》

学习经历

东北财经大学统计学专业，博士

加拿大女王大学生物统计系，联合培养博士

工作经历

2020年9月-2022年9月，中国科公司数学与系统科学研究院，博士后

2022年9月至今，qmh球盟会球盟会，副教授

主要获奖荣誉

1. 美国老员工数学建模竞赛M奖1项，H奖2项（指导教师）

2. 全国老员工统计建模大赛北京赛区（研究生组）二等奖1项，三等奖1项（指导教师）

3. 全国老员工统计建模大赛北京赛区（本科生组）一等奖1项（已进国赛），二等奖1项，三等奖1项（指导教师）

主要科研项目

主持国家自然科学基金青年项目1项，主持完成中国博士后科学基金面上项目1项，参与国家社科基金重大项目1项，国家社科基金项目1项，国家自科基金多项。主要有：

1. 国家自然科学基金青年科学基金项目，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，执行年份（2022年-2024年），30万，主持人

2. 第69批中国博士后科学基金面上项目，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，执行年份（2021年-2023年），优秀结题，5万，主持人

3. 国家社会科学基金项目，数字字金融提升产业链供应链韧性的效应、机制与实现路径研究，执行年份（2023年-2025年），20万，主要成员

4. 国家社会科学基金重大项目，大数据时代雾霾污染经济损失评估及防治对策研究，执行年份（2018年-2022年），48万，主要成员

5. 国家自然科学基金面上项目，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，执行年份（2023年-2026年），48万，主要成员

6. 国家自然科学基金面上项目，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，执行年份（2023年-2026年），48万，主要成员

7. 国家自然科学基金面上项目，大规模风电并网管理中的风能资源评估与预测研究，执行年份（2017年-2020年），48万，主要成员

主要学术成果

[1] Shi, H., Heng, J#., Duan, H., Li, H., Chen, W., Wang, P., Wang, S. Critical mineral constraints pressure energy transition and trade toward the Paris Agreement climate goals. Nature Communications, 16(1), 1-13.

[2] Wu, H., Gao, X. Z., Li, K., & Heng, J*. N. An improved brain-motivated network for forecasting day-ahead stock prices of electricity companies. Knowledge-Based Systems, 114040.

[3] 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, 118029.

[4] 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, 845-866.

[5] 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), 235.

[6] 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, (1), 3205396.

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

[8] 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, 197-209.

[9] 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, 591-613. # (ESI) 高被引文章/前1%引用文章

[10] Lu, H., Heng, J*., & Wang, C. An AI-based hybrid forecasting model for wind speed forecasting. In International Conference on Neural Information Processing (pp. 221-230). Springer, Cham.

[11] 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), 101153.

[12] 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, 121181.

[13] 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, 786-801. # (ESI) 高被引文章/ 前1%引用文章

[14] Hu, J., Luo, Q., Tang, J., Heng, J., & Deng, Y. Conformalized temporal convolutional quantile regression networks for wind power interval forecasting. Energy, 123497.

[15] Wu, H., Liang, Y., Gao, X. Z., Heng, J. N., & Du, P. AVI-Net: Audio-visual-integration inspired deep network with application to short-term air temperature forecasting. Expert Systems with Applications, 281, 127604.

[16] Wu, H., Du, P., & Heng, J. Gated convolution with attention mechanism under variational mode decomposition for daily rainfall forecasting. Measurement, 237, 115222.

[17] Wu, H., Gao, X., Heng, J., Wang, X., & Lü, X. Bionic fusion perspective: Audiovisual-motivated integration network for solar irradiance prediction. Energy Conversion and Management, 314, 118726.

[18] Wu, H., Liang, Y., Gao, X. Z., Heng, J. N., & Chen, Z. Sleep-induced Network with Reducing Information Loss for Short-term Load Forecasting. IEEE Transactions on Power Systems.

[19] Wu, H., Liang, Y., Gao, X. Z., & Heng, J. N. Bionic-inspired oil price prediction: Auditory multi-feature collaboration network. Expert Systems with Applications, 244, 122971.

[20] Wu, H., Gao, X. Z., & Heng, J. N. Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height. Energy, 294, 130887.

[21] .Zhu, M., Hong, Y., Wang, S., Cheng, Z., & Heng, J. Stock Market Volatility Forecasting: Can Interval Data Improve it?. Available at SSRN 4788758.

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

[23] Li, M., Lin, W., Wei, Y., Wang, S., & Heng, J. What affects the price of Bitcoin? Evidence from game theory and machine learning. Applied Economics Letters, 32(6), 770-774.

[24] Wu, H., Liang, Y., & Heng, J. Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting. Applied Energy, 339, 120995.

[25] Wu, H., Liang, Y., Heng, J. N., Ma, C. X., & Gao, X. Z. MSV-net: Multi-scale visual-inspired network for short-term electricity price forecasting. Energy, 291, 130350.

*为通讯作者，#为共同第一作者