基本信息
赵晶  女  硕导  中国科学院大气物理研究所
电子邮件: zhaojing@lasg.iap.ac.cn
通信地址: 北京市朝阳区北辰西路81号中国科学院大气物理研究所
邮政编码:

招生信息

   
招生专业
070601-气象学
招生方向
数值模拟与人工智能的交叉

教育背景

2014-04--2016-06   中国科学院大气物理研究所   客座学生
2013-09--2016-06   兰州大学数学与统计学院   博士
2010-09--2013-06   兰州大学数学与统计学院   硕士
2006-09--2010-06   兰州大学数学与统计学院   本科
学历

工作经历

   
工作简历
2021-08~现在, 中国科学院大气物理研究所, 副研究员
2016-07~2021-06,中国科学院大气物理研究所, 博士后

出版信息

   
发表论文
[1] 邱文智, 张文煜, 郭振海, 赵晶, 马可可. 基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测. 太阳能学报[J]. 2023, [2] 张文煜, 马可可, 郭振海, 赵晶, 邱文智. 基于灰狼算法和极限学习机的风速多步预测. 郑州大学学报(工学版)[J]. 2023, [3] Zhao, Jing, Guo, Zhenhai, Guo, Yanling, Lin, Wantao, Zhu, Wenjin. A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions. ENERGY[J]. 2021, 218: http://dx.doi.org/10.1016/j.energy.2020.119509.
[4] Chen, XueJun, Zhao, Jing, Jia, XiaoZhong, Li, ZhongLong. Multi-step wind speed forecast based on sample clustering and an optimized hybrid system. RENEWABLE ENERGY[J]. 2021, 165: 595-611, http://dx.doi.org/10.1016/j.renene.2020.11.038.
[5] Hu, Jianming, Lin, Yingying, Tang, Jingwei, Zhao, Jing. A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function. APPLIED SOFT COMPUTING[J]. 2020, 92: http://dx.doi.org/10.1016/j.asoc.2020.106327.
[6] Zhao, Jing, Wang, Jianzhou, Guo, Zhenhai, Guo, Yanling, Lin, Wantao, Lin, Yihua. Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method. APPLIED ENERGY[J]. 2019, 255: http://dx.doi.org/10.1016/j.apenergy.2019.113833.
[7] Zhao, Jing, Guo, Zhenhai, Guo, Yanling, Zhang, Ye, Lin, Wantao, Hu, Jianming. Wind resource assessment based on numerical simulations and an optimized ensemble system. ENERGY CONVERSION AND MANAGEMENT[J]. 2019, 201: http://dx.doi.org/10.1016/j.enconman.2019.112164.
[8] Ye ZHANG, Shiping YANG, Zhenhai GUO, Yanling GUO, Jing ZHAO. Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm. ATMOSPHERIC AND OCEANIC SCIENCE LETTERS[J]. 2019, 12(2): 107-115, http://lib.cqvip.com/Qikan/Article/Detail?id=65798376504849574850484853.
[9] Zhao, Jing, Guo, Yanling, Xiao, Xia, Wang, Jianzhou, Chi, Dezhong, Guo, Zhenhai. Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method. APPLIED ENERGY[J]. 2017, 197: 183-202, http://dx.doi.org/10.1016/j.apenergy.2017.04.017.
[10] 郭燕玲, 赵晶, 周林, 张文煜, 郭振海. 山东半岛风电爬坡事件的识别与天气分析研究. 气候与环境研究[J]. 2017, 22(1): 97-107, http://lib.cqvip.com/Qikan/Article/Detail?id=671297052.
[11] Wang, Jianzhou, Liu, Feng, Song, Yiliao, Zhao, Jing. A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. APPLIED SOFT COMPUTING[J]. 2016, 48: 281-297, http://dx.doi.org/10.1016/j.asoc.2016.07.011.
[12] Zhao, Jing, Guo, ZhenHai, Su, ZhongYue, Zhao, ZhiYuan, Xiao, Xia, Liu, Feng. An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. APPLIED ENERGY[J]. 2016, 162: 808-826, http://dx.doi.org/10.1016/j.apenergy.2015.10.145.
[13] Zhao, Jing, Wang, Jianzhou, Liu, Feng. Multistep Forecasting for Short-Term Wind Speed Using an Optimized Extreme Learning Machine Network with Decomposition-Based Signal Filtering. JOURNAL OF ENERGY ENGINEERING[J]. 2016, 142(3): https://www.webofscience.com/wos/woscc/full-record/WOS:000383141900009.
[14] Lu, Xing, Wang, Jianzhou, Cai, Yuan, Zhao, Jing. Distributed HS-ARTMAP and its forecasting model for electricity load. APPLIED SOFT COMPUTING[J]. 2015, 32: 13-22, http://dx.doi.org/10.1016/j.asoc.2015.03.037.
[15] Erdong Zhao, Jing Zhao, Liwei Liu, Zhongyue Su, Ning An. Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation. ENERGIES[J]. 2015, 9(7): http://oa.las.ac.cn/oainone/service/browseall/read1?ptype=JA&workid=JA202003190001000ZK.
[16] Jing Zhao, Jianzhou Wang, Zhongyue Su. Power generation and renewable potential in China. RENEWABLE AND SUSTAINABLE ENERGY REVIEWS. 2014, 40: 727-740, http://dx.doi.org/10.1016/j.rser.2014.07.211.
[17] Wang, Jujie, Wang, Jianzhou, Li, Yaning, Zhu, Suling, Zhao, Jing. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. INTERNATIONALJOURNALOFELECTRICALPOWERENERGYSYSTEMS[J]. 2014, 62: 816-824, http://dx.doi.org/10.1016/j.ijepes.2014.05.038.
[18] Chen, Xuejun, Zhao, Jing, Hu, Wenchao, Yang, Yufeng. Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method. ABSTRACT AND APPLIED ANALYSIS[J]. 2014, 2014: https://doaj.org/article/bbe78100b44440fa8799cab34238a329.
[19] Zhao, Ze, Wang, Jianzhou, Zhao, Jing, Su, Zhongyue. Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE[J]. 2012, 40(5): 525-532, http://dx.doi.org/10.1016/j.omega.2011.10.003.
[20] Guo, Zhenhai, Zhao, Jing, Zhang, Wenyu, Wang, Jianzhou. A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. ENERGY[J]. 2011, 36(3): 1668-1679, http://dx.doi.org/10.1016/j.energy.2010.12.063.

科研活动

   
科研项目
( 1 ) 广西风电场功率预测分析和评估, 负责人, 企业委托, 2023-05--2023-12
( 2 ) 基于集合预报的河北风光新能源短中期精细化数值预报技术, 参与, 地方任务, 2023-01--2023-12
( 3 ) 适用于风速预测的微分神经网络方法及其在风能利用中的应用研究, 负责人, 国家任务, 2019-01--2021-12
( 4 ) 地球系统模式试验场景系统研制, 参与, 国家任务, 2017-07--2022-06
参与会议
(1)基于集合数值模拟和深度强化学习的风电场风速预测方法   第五届中国大地测量和地球物理学学术大会   2023-04-21