Large Scale predictive analytics for real-time energy management

Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Volumn , Issue , 2013, Pages 657-664

  Balac, Natasha ^a   Sipes, Tamara ^a   Wolter, Nicole ^a   Nunes, Kenneth ^a   Sinkovits, Bob ^a   Karimabadi, Homa ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

big data; data mining; smart grid; time series

Indexed keywords

BIG DATA; COST EFFECTIVENESS; ENERGY MANAGEMENT; ENERGY UTILIZATION; INVESTMENTS; MICROGRIDS; PREDICTIVE ANALYTICS; REAL TIME SYSTEMS; TIME SERIES;

ALTERNATIVE ENERGY SOURCE; AUTOMATED BUILDINGS; BUILDING BEHAVIOR; LEGACY EQUIPMENT; REAL-TIME ENERGY MANAGEMENT; REDUCE ENERGY CONSUMPTION; RESEARCH PLATFORMS; SMART GRID;

DATA MINING;

EID: 84893303609     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2013.6691635     Document Type: Conference Paper

References (22)

1. U.S. Department of Energy. "Smart Grid / Department of Energy
2. http://www.smartgrid.gov/the-smart-grid#smart-grid
3. Washom, B., Dilliott, J., Weil, D., Kleissl, J., Balac, N., Torre, W., Richter, C., "Ivory Tower of Power: Microgrid Implementation at the University of California, San Diego," Power and Energy Magazine, IEEE, vol.11, no.4, pp. 28,32, July 2013. doi: 10.1109/MPE.2013.2258278
4. Strande, S.M., Cicotti, P., Sinkovits, R.S., William, S., Young, W.S., Wagner, R., Tatineni, M., Hocks, E., Snavely, A., Norman, M. 2012. Gordon: Design, performance, and experiences deploying and supporting a data intensive supercomputer. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the Campus and Beyond (XSEDE '12). ACM, New York, NY, USA, Article 3, 8 pages. DOI=10.1145/2335755.2335789.
5. Dean, J., Ghemawat, S. 2008. MapReduce: simplified data processing on large clusters. Commun. ACM 51, 1 (January 2008), 107-113. doi: 10.1145/1327452.1327492 http://doi.acm.org/10.1145/1327452.1327492
6. http://www.r-project.org
7. http://mahout.apache.org
8. http://hive.apache.org
9. "IBM What is big data?-Bringing big data to the enterprise". 01.ibm.com.
10. Solomon, D., Winter, R.L., Boulanger, A.G., Anderson, R.N., Wu, L.L., (2011); Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression. Technical Report Columbia University Computer Science Technical reports; CUCS-040-11; New York; http://hdl.handle.net/10022/AC:P: 12171.
11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.A. (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
12. Breiman, L., Friedman, J., Stone, C. J., Olshen, R. A. (1984). Classification and regression trees. Chapman &Hall/CRC.
13. Kalogirou S.A. 2000. Applications of artificial neuralnetworks for energy systems, Applied Energy, Volume 67, Issues 1-2, September 2000, Pages 17-35.
14. Karimabadi, H., Sipes, T. B., White, H., Marinucci, M., Dmitriev, A., Chao, L.K., Driscoll, J., Balac, N. (2007). Data Mining in Space Physics: 1. The MineTool Algorithm, J. Geophys. Res., 112, A11215, doi:10.1029/2006JA012136.
15. Myers, C. S. and Rabiner, L. R. A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal, 60(7):1389-1409, September 1981.
16. Rabiner, L. R. and Juang, B. H. An introduction to hidden markov models. IEEE Magazine on Acoustics, Speech and Signal Processing, 3(1):4-16, 1986.
17. Schmidhuber, J., Graves, A., Gomez, F. and Hochreiter, S. Recurrent Neural Networks, Cambridge University Press, 2012.
18. Kadous, M. W. Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. PhD thesis, School of Computer Science &Engineering, University of New South Wales, 2002.
19. Pérez-Amaral, T., Gallo, G. M. and White, H., A Comparison of Complementary Automatic Modeling Methods: RETINA and PcGets," Econometric Theory, 2005.
20. White, H., Personnel Readiness: Neural Network Modeling of Performance-Based Estimates, Final Report to the Office of Naval Research, Contract #: N00014-95-C-1078, 1999.
21. technique: Automated detection of flux transfer events using Cluster data, J. Geophys. Res., Vol 114, A06216, doi:10.1029/2009JA014202, 2009
22. White, H., Personnel Readiness: Neural Network Modeling of Performance-Based Estimates, Final Report to the Office of Naval Research, Contract #: N00014-95-C-1078, 1999.