국내 전력산업에서의 빅데이터 플랫폼 성과 평가 방법론 = Methodology for Evaluating Big Data Platforms Performance in the Domestic Electronic Power Industry

국문 초록 (Abstract)

국내 전력산업이 스마트 그리드화 되면서 이로 인해 발생하는 빅데이터를 활용하여 수요관리, 시설물관리, 대고객서비스 등을 위한 빅데이터 플랫폼들이 도입되고 있는 추세이다. 그러나 빅데이터 프로젝트의 속성상 실제로 빅데이터 플랫폼의 활용이 업무 프로세스 상에서 정착되기 위해서는 많은 시간과 업데이트가 필요하다.
따라서 기존에 알려져 있거나 이론적인 평가 방법으로는 초기 빅데이터 플랫폼의 성과를 평가하기는 적절하지 않다. 본 논문에서는 빅데이터의 규모, 다양성, 속도에 따른 정보의 완전성/충분성, 정보의 신뢰성/정확성, 정보의 적합성/관련성, 정보의 상세성/구체성, 정보의 비교가능성, 정보의 불편성, 정보의 적시성 등 특정 정보의 7 가지 품질 측면에서 전력산업에서 초기 빅데이터 플랫폼의 성과를 평가하는 방법론을 제시한다.

참고문헌 (Reference)

1 Mittal, A., "Trustworthiness of big data" 80 (80): 2013

2 McShea, C., "The reason so many analytics efforts fall short" 2016

3 Stein, B., "The enterprise data lake : Better integration and deeper analytics" 1 (1): 18-, 2014

4 Capgemini, "The deciding factor: Big data & decision making" Capgemini 1-24, 2017

5 Delone, W. H., "The DeLone and McLean model of information systems success : a ten-year update" 19 (19): 9-30, 2003

6 Alahakoon, D., "Smart electricity meter data intelligence for future energy systems : A survey" 12 (12): 425-436, 2015

7 Capgemini, "Smart Analytics for the Utility Sector"

8 Guilan, W., "Real-time big data technologies of energy internet platform" IEEE 1-6, 2016

9 Fotopoulou, E., "Providing personalized energy management and awareness services for energy efficiency in smart buildings" 17 (17): 2054-, 2017

10 Li, E. Y., "Perceived importance of information system success factors : A meta analysis of group differences" 32 (32): 15-28, 1997

11 Mao, R., "Overcoming the challenge of variety : big data abstraction, the next evolution of data management for AAL communication systems" 53 (53): 42-47, 2015

12 Accenture, "Optimizing Grid Performance through Advanced Operations"

13 IS Group, "Managing big data for smart grids and smart meters" IBM Corporation 2012

14 DeLone, W. H., "Information systems success : The quest for the dependent variable" 3 (3): 60-95, 1992

15 Kahn, B. K., "Information quality benchmarks : product and service performance" 45 (45): 184-192, 2002

16 Jiang, R., "Energy-theft detection issues for advanced metering infrastructure in smart grid" 19 (19): 105-120, 2014

17 Al-Salim, A. M., "Energy efficient big data networks : Impact of volume and variety" 15 (15): 458-474, 2017

18 Qi, Q., "Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison" 6 : 3585-3593, 2018

19 Ku, T. Y., "Demand response operation method on energy big data platform" IEEE 823-825, 2018

20 Ostroff, C., "Configurations of organizational effectiveness and efficiency" 36 (36): 1345-1361, 1993

21 Kadadi, A., "Challenges of data integration and interoperability in big data" IEEE 38-40, 2014

22 Zhou, K., "Big data driven smart energy management : From big data to big insights" 56 : 215-225, 2016

23 Tole, A. A., "Big data challenges" 4 (4): 31-40, 2013

24 Hofmann, E., "Big data and supply chain decisions : the impact of volume, variety and velocity properties on the bullwhip effect" 55 (55): 5108-5126, 2017

25 Bhattarai, B. P., "Big data analytics in smart grids : state-of-the-art, challenges, opportunities, and future directions" 2 (2): 141-154, 2019

26 IDC, "Big Data: Turning Promise Into Reality"

27 Marinakis, V., "Big Data for Energy Management and Energy-Efficient Buildings" 13 (13): 1555-, 2020

28 Fanning, K., "Big Data and KPIs: A Valuable Connection" 27 (27): 17-19, 2016

29 Wang, R. Y., "Beyond Accuracy : What Data Quality Means to Data Consumers" 12 (12): 5-33, 1996

30 Aldholay, A., "An extension of Delone and McLean IS success model with self-efficacy" 2018

31 Wilcox, T., "A Big Data platform for smart meter data analytics" 105 : 250-259, 2019