A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION

Hao Ying; Che Jianfeng; Dong Lei; Wang Lijie; Zhan Wenhua; Guo Hongwu

doi:10.19912/j.0254-0096.tynxb.2020-0951

PDF(1482 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (6) : 106-114. DOI: 10.19912/j.0254-0096.tynxb.2020-0951

A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION

Hao Ying¹, Che Jianfeng², Dong Lei³, Wang Lijie¹, Zhan Wenhua⁴, Guo Hongwu⁴

Author information +

History +

Abstract

Since the Numerical weather prediction （NWP） data in the power forecasting of renewable power generation is always multidimensional and redundant, dimension reduction of the NWP data is very necessary for data preprocessing before power forecasting. After the feature selection and feature conversion algorithm being applied for NWP dimension reduction, a general reliability assessment of NWP dimension reduction based on neighborhood preservation was proposed. This evaluation criterion is based on the neighborhood changes of sample points in the process of data dimensionality reduction projection, without considering the dimensionality reduction principle and objective function of the algorithm itself, which can effectively evaluate the reliability of different dimensionality reduction algorithms in NWP data dimensionality reduction, thus providing theoretical basis for other researchers to select NWP algorithm.

Key words

numerical weather prediction / date dimension reduction / neighborhood preservation / dimension reduction trustworthiness / evaluation criterion

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Hao Ying, Che Jianfeng, Dong Lei, Wang Lijie, Zhan Wenhua, Guo Hongwu. A RELIABILITY EVALUATION CRITERION FOR DIMENSION REDUCTION OF NUMERICAL WEATHER PREDICTION DATA BASED ON NEIGHBORHOOD PRESERVATION[J]. Acta Energiae Solaris Sinica. 2022, 43(6): 106-114 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0951

References

[1] 康重庆, 姚良忠. 高比例可再生能源电力系统的关键科学问题与理论研究框架[J]. 电力系统自动化, 2017, 41(9): 2-11.
KAND C Q, YAO L Z.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J]. Automation of electric power systems, 2017, 41(9): 2-11.
[2] 李志伟. 风光高占比多能源电力系统随机优化调度研究[D]. 保定: 华北电力大学, 2019.
LI Z W.Research on stochastic optimization scheduling ofmulti-energy power system with high proportion of wind power and photocoltaic power generation[D]. Baoding: North China Electric Power University, 2019.
[3] 中国储能网新闻中心. 2019年全球新增可再生能源装机容量176吉瓦[EB/OL].(2020-04-07) http://www.escn.com.cn/news/show-941279.html.
China Energy Storage Network News Center. The world added 176 gigawatts of installed renewable energy capacity in2019 [EB/OL]. (2020-04-07) http://www.escn.com.cn/news/show-941279.html.
[4] 冯双磊, 王伟胜, 杨明, 等. 新能源发电功率预测技术[R]. 中国电机工程学会专题技术报告, 2018: 237-312.
FENG S L, WANG W S, YANG M, et al.Forecasting technology of renewable power generation[R]. Special Technical Report of Chinese Society of Electrical Engineering, 2018: 237-312.
[5] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
DING M, WANG W S, WANG X L, et al.A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE, 2014, 34(1): 1-14.
[6] 辛禾. 考虑多能互补的清洁能源协同优化调度及效益均衡研究[D]. 北京: 华北电力大学, 2019.
XIN H.The synergetic optimal scheduling and benefit equlibrium model for clean energy absorptive considering multi-energy hybrid[D]. Beijing: North China Electric Power University, 2019.
[7] 国家发展改革委. 清洁能源消纳行动计划(2018—2020年) [EB/OL]. (2018-10-30) https://www.ndrc.gov.cn/xxgk/cfb/ghxwj/201812/t20181204_960958.html.
National Development and Reform Commission. The absorption plan for clean energy (2018—2020年) [EB/OL]. (2018-10-30) https://www.ndrc.gov.cn/xxgk/zcfb/ghxwj/201812/t20181204_960958.html.
[8] 王晛, 王留晖, 张少华. 风电商与DR聚合商联营对电力市场竞争的影响[J]. 电网技术, 2018, 42(01): 110-116.
WANG X, WANG L H, ZHANG S H.Impacts of cooperation between wind power producer and DR aggregator on electricity market equilibrium[J]. Power system technology, 2018, 42(1): 110-116.
[9] 薛儒涛. 含分布式电源的业扩报装最优供电方案研究[D]. 保定: 华北电力大学, 2019.
XUE R T.Research on optimal power supply scheme for power business expanding with distributed generation[D]. Baoding: North China Electric Power University, 2019.
[10] 冬雷, 廖晓钟, 王丽婕. 大型风电场发电功率建模与预测[M]. 北京: 科学出版社, 2014.
DONG L, LIAO X Z, WANG L J.Power modeling and forecasting for large wind farm[M]. Beijing: China Science Publishing & Media Ltd., 2014.
[11] DONG L, WANG L, KHAHRO S, et al.Wind power day-ahead prediction with cluster analysis of NWP[J]. Renewable & sustainable energy reviews, 2016, 60: 1206-1212.
[12] HAO Y, DONG L, LIAO X Z, et al.A novel clustering algorithm based on mathematical morphology for wind power generation prediction[J]. Renewable energy, 2019, 136: 572-585.
[13] GAO S, DONG L, TIAN C W, et al.Wind power prediction using time-series analysis base on rough sets[J]. Electric information and control engineering, 2011, 3: 2847-2852.
[14] CHRISTINA S, WOLF-GERRIT F.Wind forecasting using principal component analysis[J]. Renewable energy, 2014, 69: 365-374.
[15] MARIA M, MARIA G D G, PAOL M C. Forecasting of PV power generation using weather input data-preprocessing techniques[J]. Energy procedia, 2017, 126: 651-658.
[16] GU J, WANG Y N, XIE D, et al.Wind farm NWP data preprocessing method based on t-SNE[J]. Energies, 2019, 12: 3622.
[17] 汪超永. 基于特征学习的高维数据处理与预测[D]. 合肥: 中国科学技术大学, 2015.
WANG C Y.High-dimensional data processing and forecasting based on fuature learning[D]. Hefei: University of Science and Technology of China, 2015.
[18] KALIDINDI S R, DE G M.Materials data science: current statue and future outlook[J]. Annual review of materials reserch, 2015, 45: 171-193.
[19] WEST D M.Big data for educatuon: data mining, data analytics, and web dashboards[J]. Governance studies at brookings, 2012, 4: 1-10.
[20] PEARSON K.Notes on the history of correlation[J]. Biometrika, 1920, 13(1): 25-45.
[21] 金林, 李研. 几种相关系数辨析及其在R语言中的实现[J]. 统计与信息论坛, 2019, 34(4): 3-11.
JIN L, LI Y.Discrimination of several correlation coefficients and their implementation in R software[J]. Statistics & information forum, 2019, 34(4): 3-11.
[22] SPEARMAN C.The proof and measurement of association between two things[J]. The American journal of peychology, 1904, 15(1): 441-471.
[23] KENDALL M G.A new measurement of rank correlation[J]. Biometrika, 1938, 30: 81-93.
[24] 王海雷. 面向高维数据的特征学习算法研究[D]. 合肥: 中国科学技术大学, 2019.
WANG H L.Research on feature learning algorithm for high-dimensional data[D]. Hefei: University of Science and Technology of China, 2019.
[25] 熊音笛, 刘开培, 秦亮, 等. 基于时序数据动态天气划分的短期风电功率预测方法[J]. 电网技术, 2019, 43(9): 3353-3359.
XIONG Y D, LIU K P, QIN L, et al.Short-term wind power prediction method based on dynamic wind power weather division of time sequence data[J]. Power system technology, 2019, 43(9): 3353-3359.
[26] 余映, 王斌, 张立明. 一种面向数据学习的快速PCA算法[J]. 模式识别与人工智能, 2009, 22(4): 567-573.
YU Y, WANG B, ZHANG L M.A fast data-oriented algorithm for principal component analysis[J]. Pattern recognition and artificial intelligence, 2009, 22(4): 567-573.
[27] 王丽婕, 冬雷, 高爽. 基于多位置NWP与主成分分析的风电功率短期预测[J]. 电工技术学报, 2015, 30(5): 79-84.
WANG L J, DONG L, GAO S.Wind power short-term prediction based on principal component analysis of nwp of multiple locations[J]. Transactions of China Electrotechnical Society, 2015, 30(5): 79-84.
[28] KASKI S, NIKKILÄ J, OJA M, et al.Trustworthiness and metrics in visualizing similarity of gene expression[J]. BMC bioinformatics, 2003, 4: 1-13.
[29] SAMUELK, SAMUE K.Visualizing gene interaction graphs with local multidimensional scaling[C]//European Symposium on Esann, DBLP, 2006: 1-8.
[30] 刘丽娜, 马世伟, 芮玲. 基于可信赖性和连续性的流形降维效果评价方法[J]. 计算机应用研究, 2018, 35(6): 1707-1711.
LIU L N, MA S W, RUI L.Evaluation method for manifold dimensionality reduction effect based on trustworthiness and continuity[J]. Application research of computers, 2018, 35(6): 1707-1711.
[31] JAR K V, SAMUE K.Neighborhood preservation in nonlinear projection methods: an experimental study[C]//International Conference on Artificial Neural Networks, Springer-Verlag, 2001: 1-8.