王晨老师简介
工作及教育经历
2020.10-2022.10，中山大学，计算机应用技术，博士后
2017.09-2020.08，兰州大学，计算机应用技术，工学博士
2015.09-2017.06，兰州大学，应用统计，应用统计硕士
2009.09-2013.06，兰州财经大学，统计学，经济学学士
研究方向
计算机应用、大数据挖掘、概率论与数理统计、多目标优化
个人自述
近年来本人研究基于概率论与数理统计和人工智能的统计建模，并应用在风能和空气质量预测领域的应用，以解决预测问题的不确定性和精度，本人的研究工作主要有四个部分：1）采用非参数假设检验的方法分析数据差异性，并利用参数检验分析和筛选最优人工智能预测模型，采用概率密度函数和置信区间分析人工智能预测模型的不确定性，2）建立概率统计统计模型分析风能转化的不确定性区间，3）利用概率统计方法分析风电场在不同环境下运行过程中的可靠性，4）研究不同概率密度函数在储能寿命分析的有效性，并制定储能系统优化目标；研究多目标优化算法对储能系统经济性和装机量的优化流程；根据优化结果制定储能配置方案。本人在各类学术期刊发表预测相关文章共35篇，其中以第一作者或通讯作者发表 SCI检索国际期刊论文17篇，其中JCR：Q1共7篇，包括Expert Systems With Applications (SCI, IF: 8.665, JCR: Q1)、 Information Sciences (IF: 8.233 JCR: Q1)、Applied Soft Computing (IF: 8.634, JCR: Q1)、Energy Conversion and Management (IF: 11.533, JCR: Q1)、Applied Energy (SCI, IF: 11.446, JCR: Q1)、Renewable Energy (SCI, IF: 8.634, JCR: Q1)等。截止到2022年12月，本人的Scopus总被引次数为1014次、h指数为17 (其中 3 篇论文被引次数超过40次，6篇超过60次)，单篇最高被引次数为95次。1篇学术论文被美国ISI Web of science 基本科学指标ESI列为领域学科的高引用论文(Highly Cited Papers)和研究前沿。
研究项目经历
2022.11 广东省基础与应用基础研究青年基金项目《风电场运行功率评估及预测和储能装机容量优化配置》（主持，在研）。
2018.01-2022.12国家社科基金重大项目《大数据时代雾霾污染经济损失评估及防治对策研究》（参与，已完结）。
2020.01-2022.12 横向项目《应用于新能源场群调度辅助决策的数字孪生关键技术研究》（参与，在研）。
2017.01-2020.12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的评估与预测研究》（参与，已完结）。
2018.01-2021.12国家社会科学基金项目一般项目 《基于大数据人工智能的风电并网管理中风能资源评估与预测研究》（参与，已完结）。
学术兼职
Applied Energy、 Renewable Energy、 Applied Mathematical Modelling、 Energy 等6个期刊的审稿人
近五年发表论文
1. Wang, C., Zhang, S., Liao, P., Fu, T. (2022). Wind speed forecasting based on hybrid model with model selection and wind energy conversion. Renewable Energy, 2022, 196, pp. 763–781 (SCI, IF: 8.634, JCR: Q1)
2. Zhang, S., Wang, C.*, Liao, P., Xiao, L., Fu, T. (2022). Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory. Expert Systems with Applications, 2022, 193, 116509 (SCI, IF: 8.665, JCR: Q1)
3. Huang, Y., Deng, Y.*, Wang, C.*, Fu, T. (2021). Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants. Frontiers in Environmental Science, 2021, 9, 761287 (SCI, IF: 5.411, JCR: Q2)
4. Wang, C., Zhang, S., Xiao, L., Fu, T. (2021). Wind speed forecasting based on multi-objective grey wolf optimisation algorithm, weighted information criterion, and wind energy conversion system: A case study in Eastern China. Energy Conversion and Management, 2021, 243, 114402 (SCI, IF: 11.533, JCR: Q1)
5. Zhou, Q. (博士研究生导师), Wang, C.*, Zhang, G. (2020). A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed. Applied Soft Computing, 2020, 94, 106463 (SCI, IF: 8.263, JCR: Q1)
6. Zhou, Q. (博士研究生导师), Wang, C.*, Zhang, G. (2019). Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems. Applied Energy, 2019, 250, pp. 1559–1580 (SCI, IF: 11.446, JCR: Q1)
7. Xiao, L., Wang, C.*, Dong, Y., Wang, J. (2019). A novel sub-models selection algorithm based on max-relevance and min-redundancy neighborhood mutual information. Information Sciences, 2019, 486, pp. 310–339 (SCI, IF: 8.233 JCR: Q1)
8. Zhao, X. (硕士研究生导师), Wang, C.*, Su, J., Wang, J. (2019). Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system. Renewable Energy, 2019, 134, pp. 681–697 (SCI, IF: 8.634, JCR: Q1)
9. Fu, T., Wang, C.*. (2018). A hybrid wind speed forecasting method and wind energy resource analysis based on a swarm intelligence optimization algorithm and an artificial intelligence model. Sustainability (Switzerland), 2018, 10(11), 3913 (SCI, IF: 3.889, JCR: Q2)
10. Wang, J., Wang, C.*, Zhang, W. (2018). Data analysis and forecasting of tuberculosis prevalence rates for smart healthcare based on a novel combination model. Applied Sciences (Switzerland), 2018, 8(9), 1693 (SCI, IF: 2.838, JCR: Q2)
11. Yao, Z., Wang, C.* (2018). A hybrid model based on a modified optimization algorithm and an artificial intelligence algorithm for short-term wind speed multi-step ahead forecasting. Sustainability (Switzerland), 2018, 10(5), 1443 (SCI, IF: 3.889, JCR: Q2)
12. Fu, T., Wang, C.* (2018). A Novel Ensemble Wind Speed Forecasting Model in the Longdong Area of Loess Plateau in China. Mathematical Problems in Engineering, 2018, 2018, 2506157 (SCI, IF: 1.430, JCR: Q3)
13. Wang, J., Zhang, L., Wang, C., Liu, Z. (2021). A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting. Applied Soft Computing, 2021, 113, 107941 (SCI, IF: 8.263, JCR: Q1)
14. Yong, B., Wang, C., Shen, J., Yin, H., Zhou, R. (2021). Automatic ventricular nuclear magnetic resonance image processing with deep learning. Multimedia Tools and Applications, 2021, 80(26-27), pp. 34103–34119 (SCI, IF: 2.577, JCR: Q2)
15. Fu, T., Zhang, S., Wang, C. (2020). Application and research for electricity price forecasting system based on multi-objective optimization and sub-models selection strategy. Soft Computing, 2020, 24(20), pp. 15611–15637 (SCI, IF: 3.732, JCR: Q2)
16. Ma, T., Wang, C., Wang, J., Cheng, J., Chen, X. (2019). Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China. Information Sciences, 2019, 505, pp. 157–182 (SCI, IF: 8.233 JCR: Q1)
17. Wang, J., Bai, L., Wang, S., Wang, C. (2019). Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system. Journal of Cleaner Production, 2019, 234, pp. 54–70 (SCI, IF: 11.072, JCR: Q1)
会议论文
1. Zhou, R., Li, X., Yong, B., Shen, Z., Wang, C. Zhou, Q. Cao, Y., Li, K.-C. (2019). Arrhythmia recognition and classification through deep learning-based approach. International Journal of Computational Science and Engineering, 2019, 19(4), pp. 506–517
2. Yong, B., Qiao, F., Wang, C., ...Wei, Y., Zhou, Q. (2019). Ensemble Neural Network Method for Wind Speed Forecasting. IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, 2019, 2019-October, pp. 31–36, 9020410
• 著作
1. 王建州，王晨，李洪敏，杨胡芳，《基于群智能优化算法的预测理论于方法的研究及应用》，科学出版社，2020年