Please wait a minute...
Data Analysis and Knowledge Discovery  2016, Vol. 32 Issue (12): 57-65    DOI: 10.11925/infotech.1003-3513.2016.12.08
Orginal Article Current Issue | Archive | Adv Search |
Analyzing Return of Investment for New Energy Project with Big Data: Case Study of SG-ERP System in Y City
Qian Gao1,Yang Yang2,Guangwei Hu3(),Chao Xu1,Gaofeng Shen4,Jian Zhao5
1State Grid Jiangsu Electric Power Company, Nanjing 210024, China
2School of Business, Nanjing University, Nanjing 210093, China
3School of Information Management, Nanjing University, Nanjing 210093, China
4Electric Power Economic Technology Research Institute, State Grid Jiangsu Electric Power Company, Nanjing 210018, China
5State Grid Yangzhou Electric Power Company, Yangzhou 225008, China
Export: BibTeX | EndNote (RIS)      

[Objective]This paper establishes data extraction model and evaluation mechanism for the return of investment analysis on new energy projects. All data is from the State Grid Jiangsu Electric Power Company in China. [Methods] First, we proposed a new big data management framework based on the State Grid Jiangsu Electric Power Company SG-ERP system architecture. Second, we extracted evaluative data based on Golden Gate technology, and constructed an evaluation system covering the economic, social and environmental aspects of the target projects at different development stages (i.e. decision-making, construction and operation). Finally, we examined the proposed system with the Delphi Law. [Results] We got the weight of variation coefficient and the economic, social and environmental benefits of new energy projects of Y City in 2015. [Limitations] The classification schemes for the evaluation criteria could be further refined. [Conclusions] The proposed system can evaluate the return of investments for new energy power grid projects. The data extraction method, evaluation system and weight algorithm could be used in other studies.

Key wordsPower big data      Data extraction      New energy project      Investment benefit     
Received: 25 July 2016      Published: 22 January 2017

Cite this article:

Qian Gao, Yang Yang, Guangwei Hu, Chao Xu, Gaofeng Shen, Jian Zhao. Analyzing Return of Investment for New Energy Project with Big Data: Case Study of SG-ERP System in Y City. Data Analysis and Knowledge Discovery, 2016, 32(12): 57-65.

URL:     OR

[1] 戚宇林, 荆霜雁. 我国电力通信网管理信息系统的现状及发展[J]. 电力情报, 2000(3): 15-18.
[1] (Qi Yulin, Jing Shuangyan.Current Situation and Development of Management Information System of Electric Power Communication Network in China[J]. Information of Electric Power, 2000(3): 15-18.)
[2] 郭晓利, 曲朝阳, 李晓栋, 等. 基于SOM聚类的电网可视化数据挖掘模型[J]. 情报科学, 2012, 30(2): 206-209.
[2] (Guo Xiaoli, Qu Zhaoyang, Li Xiaodong, et al.Visual Data Mining Model in Power Grid Based on SOM Clustering[J]. Information Science, 2012, 30(2): 206-209.)
[3] 李鹏, 吴江. 电网情报平台的模糊层次平衡计分绩效评价方法研究[J]. 情报科学, 2014, 32(7): 86-91.
[3] (Li Peng, Wu Jiang.Research on Performance Evaluation of Grid Information Platform Based on Fuzzy Hierarchy Balanced Scorecard Method[J]. Information Science, 2014, 32(7): 86-91.)
[4] 韦嵘晖. 大型央企集团技术竞争情报系统一体化管理运维研究[J]. 图书情报知识, 2012(1): 18-21.
[4] (Wei Ronghui.Research on Integrated Management, Operation and Maintenance of Competitive Technical Intelligence System for Large Central State-owned Enterprise Groups[J]. Document Information & Knowledge, 2012(1): 18-21.)
[5] Davidson I E.Evaluation and Effective Management of Non-technical Losses in Power Systems[J]. The Transactions of the South African Institute of Electrical Engineers, 2003, 94(3): 39-42.
[6] Sun H.Integrated Modeling of Electric Power System Operations and Electricity Market Risks with Applications[J]. Dissertation Abstracts International, 2006, 45(3): 35-52.
[7] 蔡光宗, 尚珊珊. 国家电网项目运营后评估研究[J]. 科技管理研究, 2014, 34(2): 199-204.
[7] (Cai Guangzong, Shang Shanshan.Research on the Establishment of Post Evaluation System for the Operation Process of Power Grid Project[J]. Science and Technology Management Research, 2014, 34(2): 199-204.)
[8] 刘胜利, 曹阳, 冯跃亮, 等. 配电网投资效益评价与决策模型研究及应用[J]. 电力系统保护与控制, 2015, 43(2): 119-125.
[8] (Liu Shengli, Cao Yang, Feng Yueliang, et al.Research and Application of Distribution Grid Investment Effectiveness Evaluation and Decision-making Model[J]. Power System Protection and Control, 2015, 43(2): 119-125.)
[9] Albert R, Albert I, Nakarado G L.Structural Vulnerability of the North American Power Grid[J]. Physical Review E, 2004, 69(2): 025103.
[10] Karki R, Hu P, Billinton R.A Simplified Wind Power Generation Model for Reliability Evaluation[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 533-540.
[11] 唐永胜, 王良, 李晨, 等. 基于改进灰色关联度赋权方法的电网投资效益评价模型[J]. 水电能源科学, 2013, 31(5): 186-189.
[11] (Tang Yongsheng, Wang Liang, Li Chen, et al.Investment Benefit Evaluation Model of Grid Enterprise Based on Improved Gray Correlation Degree Method[J]. Water Resources & Power, 2013, 31(5): 186-189.)
[12] Karki R, Hu P, Billinton R.A Simplified Wind Power Generation Model for Reliability Evaluation[J]. IEEE Transactions on Energy Conversion, 2006, 21(2): 533-540.
[13] 郑称德, 王全胜, 陈曦. 我国企业ERP系统实施的业务流程绩效实证研究[J]. 情报杂志, 2010, 29(1): 68-72.
[13] (Zheng Chengde, Wang Quansheng, Chen Xi.Empirical Study on Business Process Performance of ERP System in Chinese Enterprises[J]. Journal of Intelligence, 2010, 29(1): 68-72.)
[14] 郑海雁, 金农, 季聪, 等. 电力用户用电数据分析技术及典型场景应用[J]. 电网技术, 2015, 39(11): 3147-3152.
[14] (Zheng Haiyan, Jin Nong, Ji Cong, et al.Data Analysis Technology and Typical Application of Electric Power User[J]. Power System Technology, 2015, 39(11): 3147-3152.)
[15] Naumann F, Bilke A, Bleiholder J, et al.Data Fusion in Three Steps: Resolving Inconsistencies at Schema, Tuple, and Value-level[J]. Bulletin of the Technical Committee on Data Engineering, 2010(2): 21-31.
[16] Solano M A, Ekwaro-Osire S, Tanik M M.High-Level Fusion for Intelligence Applications Using Recombinant Cognition Synthesis[J]. Information Fusion, 2012, 13(1): 79-98.
[17] Jagadish H V, Gehrke J, Labrinidis A, et al.Big Data and Its Technical Challenges[J]. Communications of the ACM, 2014, 57(7): 86-94.
[18] Lin G, Liang J, Qian Y.An Information Fusion Approach by Combining Multigranulation Rough Sets and Evidence Theory[J]. Information Sciences, 2015, 314: 184-199.
[19] 杨华飞, 李栋华, 程明. 电力大数据关键技术及建设思路的分析和研究[J]. 电力信息与通信技术, 2015, 13(1): 7-10.
[19] (Yang Huafei, Li Donghua, Cheng Ming.Analysis and Research on Key Technologies and Construction Ideas of Power Big Data[J]. Power Information & Communication Technology, 2015, 13(1): 7-10.)
[20] Gulhar M, Kibria G K, Albatineh A N, et al.A Comparison of Some Confidence Intervals for Estimating the Population Coefficient of Variation: A Simulation Study[J]. Statistics and Operations Research Transactions, 2012, 36(1): 45-68.
[1] Jinzhu Zhang,Yiming Hu. Extracting Titles from Scientific References in Patents with Fusion of Representation Learning and Machine Learning[J]. 数据分析与知识发现, 2019, 3(5): 68-76.
[2] Jiang Lin,Wang Dongbo. Automatically Detecting and Tagging Foreign Language Citation Metadata[J]. 数据分析与知识发现, 2017, 1(1): 47-54.
[3] Shao Zengrong,Li Ying,Fan Tijun. The Application of Regular Expressions in Online Oil Price Event[J]. 现代图书情报技术, 2009, 3(2): 83-88.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938