Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid
G. Escobedo, Norma Jacome, G. Arroyo-Figueroa
2016
Abstract
Smart Grid is the modernization of electrical networks using intelligent systems and information technologies. The growing interest that the smart grid is attracting and its multidisciplinary nature motivate the need for solutions coming from different fields of knowledge. Due to the complexity, and heterogeneity of the smart grid and the high volume of information to be processed, Business Intelligence and Data Analytics (BI&DA) appear to be some of the enabling technologies for its future development and success. The aim of this article is proposed a framework for the development of BI&DA techniques applied to the different issues that arise in the smart grid development. As case study the paper presents the applications of BI&DA in database of processes security for Distribution System. The goal is to have available and timely information to make better decisions, to reduce the number of accidents and incidents. This work is therefore devoted to summarize the most relevant challenges addressed by the smart grid technologies and how BI&DA systems can contribute to their achievement.
References
- Zeyar Aung, “Database Systems for the Smart Grid”, Book Smart Grids: Opportunities, Developments, and Trends, Sringer Verlag, pp 151-168, 2013.
- Victor Chang, Yen-Hung Kuo, Muthu Ramachandran, “Cloud computing adoption framework: A security framework for business clouds”, Future Generation Computer Systems, Volume 57, Pages 24-41, 2016.
- S. Chaudhuri, U. Dayal and V. Narasayya, “An Overview of Business Intelligence Technology”, Communications of the ACM, Vol. 54, No. 8, pp: 88- 98, August 2011.
- Oleg Gulich, “Technological and Business challenges of Smart Grids”, Msc Theses, Lappeenranta University of Technology, 2010.
- Hsinchun Chen, Roger H. L. Chiang and Veda C. Storey, “Business Intelligence and Analytics from Big Data to Big Impact”, MIS Quarterly, Vol. 36, No. 4, pp: 1165- 1188, December 2012.
- N. Jacome-Grajales, G. Escobedo-Briones and E. Guadarrama Villa “Inteligencia de Negocios en el area de seguridad de la CFE” (In Spanish), Congreso Internacional sobre Innovación y Desarrollo Tecnológico, pp. 677-685, 2011.
- Manju Khanna, N. K. Srinath, and J. K. Mendiratta, “Data Mining in Smart Grids-A Review”, Vol. 5, No. 3, pp: 709-712, 2015.
- I. Martin-Rubio, A. E. Florence-Sandoval, J. JimenezTrillo, and D. Andina, “From Smart Grids to Business Intelligence, a Challenge for Bioinspired Systems”, Lecture Notes in Computer Science, Vol. 9108, pp 439-450, 2015.
- M. Mejía-Lavalle, G. Arroyo-Figueroa, E. F. Morales, “Innovative applications of diagnosis, forecasting, pattern recognition, and knowledge discovery in power systems”, IEEE Power & Energy Society General Meeting PES'09, Otawa Canada, pp. 1-9, 2009.
- J. Morais,, Y. Pires,, C. Cardoso, A. Klautau “An overview of data mining techniques applied to power systems. In: J. Ponce, A. Karahoca (eds.) Data Mining and Knowledge Discovery in Real Life Applications. ITech Education and Publishing, 2009.
- NIST Framework and Roadmap for Smart Grid Interoperability Standards N. S. P. 1108., Release 1.0, January 2010.
- M. Obeidat, M. North, R. Richardson V. Rattanak and S. North, “Business Intelligence Technology, Applications, and Trends”, International Management Review, Vol. 11, No.2, pp: 47-55, 2015.
- Sarvapali D. Ramchurn, Perukrishnen Vytelingum, Alex Rogers, and Nicholas R. Jennings, “Putting the 'Smarts' into the Smart Grid: A Grand Challenge for Artificial Intelligence”, Communications of the ACM, Vol. 55, No. 4, pp: 86-97, April 2012.
- William Yeoh, Andy Koronios, “Critical Success Factors for Business Intelligence Systems”, Journal of Computer Information Systems, pp:23-32, 2010.
- H. Wang, and S. Wang, “A Knowledge Management Approach to Data Mining Process for Business Intelligence,” Industrial Management & Data Systems, Vol. 108, No. 5, pp: 622-634, 2008.
- H. J. Watson, H. Barbara Wixom, The current state of business intelligence, Computer, Vol. 40, No. 9, 96-99, 2007.
Paper Citation
in Harvard Style
Escobedo G., Jacome N. and Arroyo-Figueroa G. (2016). Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid . In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: RAIBS, (IOTBD 2016) ISBN 978-989-758-183-0, pages 489-496. DOI: 10.5220/0005936604890496
in Bibtex Style
@conference{raibs16,
author={G. Escobedo and Norma Jacome and G. Arroyo-Figueroa},
title={Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: RAIBS, (IOTBD 2016)},
year={2016},
pages={489-496},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005936604890496},
isbn={978-989-758-183-0},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: RAIBS, (IOTBD 2016)
TI - Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid
SN - 978-989-758-183-0
AU - Escobedo G.
AU - Jacome N.
AU - Arroyo-Figueroa G.
PY - 2016
SP - 489
EP - 496
DO - 10.5220/0005936604890496