The Community for Technology Leaders
RSS Icon
Issue No.05 - Sept.-Oct. (2013 vol.28)
pp: 50-55
Natasha Balac , San Diego Supercomputer Center
No longer is the smart grid an esoteric, utopian idea: it's actively being put into practice, with an abundance of opportunities.
intelligent systems, smart grid, energy management system, sustainability, EMS, electric vehicles, Big Data,
Natasha Balac, ""Green Machine" Intelligence: Greening and Sustaining Smart Grids", IEEE Intelligent Systems, vol.28, no. 5, pp. 50-55, Sept.-Oct. 2013, doi:10.1109/MIS.2013.127
1. S.D. Ramchurn,P. Vytelingum,A. Rogers,, and N.R. Jennings,“Putting the ‘Smarts’ into the Smart Grid: A Grand Challenge for Artificial Intelligence.” Comm. ACM, vol. 55, no. 4, 2012, pp. 86-97.
2. US Dept. of Energy, The Smart Grid: An Introduction, 2013; .
3. S. Bashash et al., “Plug-In Hybrid Electric Vehicle Charge Pattern Optimization for Energy Cost and Battery Longevity,” J. Power Sources, vol. 196, no. 1, 2011, pp. 541-549.
4. B. Lunz,H. Walz,, and D.U. Sauer,“Optimizing Vehicle-to-Grid Charging Strategies Using Genetic Algorithm under the Consideration of Battery Aging,” Proc. Vehicle Power and Propulsion Conf., IEEE, 2011; doi:10.1109VPPC.2011.6043021.
5. S., Bashash,S.J. Moura,, and H.K. Fathy,“Battery Health-Conscious Plug-In Hybrid Electric Vehicle Grid Demand Prediction,” Proc. ASME 2010 Dynamic Systems and Control Conf., Am. Soc. Mechanical Eng. (ASME), 2010; DSCC10_GridPHEVLoad.pdf.
6. R.C. Green,L. Wang,, and M. Alam,“Applications and Trends of High Performance Computing for Electric Power Systems: Focusing on Smart Grid,” IEEE Trans. Smart Grid, vol. 4, no. 2, June 2013, pp. 922-931; doi:10.1109TSG.2012.2225646.
7. C. Rudin et al., “Machine Learning for the New York City Power Grid,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 2, 2012, pp. 328-345.
8. B. Washom et al., “Ivory Tower of Power: Microgrid Implementation at the University of California, San Diego,” IEEE Power and Energy Magazine, vol. 11, no. 4, 2013, pp. 28-32; doi:10.1109MPE.2013.2258278.
9. T. Sipes,N. Balac,H. Karimabadi,N. Wolter,K. Nunes,A. Roberts,“A Multivariate Time Series Classification Method for Streaming Data Using Temporal Metafeature Abstract Action,” Int’l J. Semantic Computing (IJSC), vol. 7, no. 2, 2013, pp. 173-183; doi:10.1142S1793351X13400084.
10. R. Mallik,N. Sarda,, and H. Kargupta,“Distributed Data Mining for Sustainable Smart Grids,” ACM Proc. Sustainable KDD Workshop, ACM, 2011; cfp.html.
11. A. Motamedi,H. Zareipour,, and W.D. Rosehart,“Electricity Price and Demand Forecasting in Smart Grids,” IEEE Trans. Smart Grids, vol. 3, no. 2, 2011, pp. 664-674.
12. H. Karimabadi et al., “A New Multivariate Time Series Data Analysis Technique: Automated Detection of Flux Transfer Events Using Cluster Data,” J. Geophysical Research, vol 114, no. A06216, 2009; doi:10.10292009JA014202.
13. H. Karimabadi et al., “Data Mining in Space Physics: 1. The MineTool Algorithm,” J. Geophysical Research, vol. 112, no. A11215, 2007; doi:10.10292006JA012136.
14. T.B. Sipes and H. Karimabadi,“MineTool-M2: An Algorithm for Data Mining of 2D Simulation Data,” Proc. Int’l Conf. Data Mining, CSREA Press, 2012;
15. T.B. Sipes and H. Karimabadi,“MineTool-3DM2: An Algorithm for Data Mining of 3D Simulation Data,” Proc. Int’l Conf. Data Mining, CSREA Press, 2013;
16. P. Mathiesen and J. Kleissl,“Evaluation of Numerical Weather Prediction for Intra-Day Hourly Solar Irradiance Forecasting in the CONUS,” Solar Energy, vol. 85, no. 5, 2011, pp. 9667-977.
17. N. Sharma et al., “Predicting Solar Generation from Weather Forecasts Using Machine Learning,” Proc. 2011 IEEE Int’l Conf. Smart Grid Communications, IEEE, 2011, pp. 528-533; doi:10.1109SmartGridComm.2011.6102379.
18. R.H. Inman,H.T.C. Pedro,, and C.F.M Coimbra,“Solar Forecasting Methods for Renewable Energy Integration,” Progress in Energy and Combustion Science, vol. 39, no. 6, 2013, pp. 535-576.
19. B. Aksanli,J. Venkatesh.,, and T. Simunic Rosing,“Datacenter Modeling and Simulation with Focus on Energy Efficiency and Green Energy Integration,” Computer, vol. 45, no. 9, 2012, pp. 56-64.
20. G. Dhiman.,R. Ayoub,, and T. Simunic Rosing,“Energy and Thermally Aware Scheduling in Datacenters,” Energy-Efficient Distributed Computing, ch. 11, Wiley-Interscience, 2010; doi:10.10029781118342015.ch11.
21. E. Regini,D. Lim,, and T.S. Rosing,“Resource Management in Heterogeneous Wireless Sensor Networks,” J. Low-Power Electronics (JOLPE), vol. 7, 2011; doi:10.1166jolpe.2011.1122.
22. Y. Agarwal,T. Weng,, and R.K. Gupta,“The Energy Dashboard: Improving the Visibility of Energy Consumption at a Campus-Wide Scale,” Proc. 1st ACM Workshop on Embedded Sensing Systems for Energy Efficiency in Buildings, ACM, 2009, pp. 55-60.
23. Y. Agarwal,S. Savage,, and R. Gupta,“SleepServer: A Software-Only Approach for Reducing the Energy Consumption of PCs Within Enterprise Environments,” Proc. 2010 Usenix Conf., Usenix Assoc., 2010, pp. 22-22.
24. Y. Agarwal et al., “Occupancy-Driven Energy Management for Smart Building Automation,” Proc. 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, ACM, 2010; .
25. Y. Agarwal et al., “Building Depot: An Extensible and Distributed Architecture for Building Data Storage, Access and Sharing,” Proc. 4th ACM Workshop on Embedded Sensing Systems for Energy-efficiency in Buildings, ACM, 2012; doi:10.11452422531.2422545.
26. X. Fang et al., “Smart Grid—The New and Improved Power Grid: A Survey,” IEEE Comm. Surveys and Tutorials, vol. 14, no. 4, 2011, pp. 944-980.
47 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool