Big Data Applications - Big Data: Related Technologies, Challenges and Future Prospects - page 75

transmission-transformation-distribution-consumption, which could not adjust
the generation capacity according to the demand of power consumption, thus
leading to electric energy redundancy and waste. To this end, smart electric
meters are developed to enable the interaction between power consumption
and power generation, and to improve power supply efficiency. TXU Energy
has widely deployed smart electric meters with a big success. Power supply
companies can read power utilization data every other 15 min other than every
month in the past. Therefore, labor cost for meter reading is saved and, because
power utilization data (a source of big data) are frequently and rapidly acquired
and analyzed, power supply companies can adjust the electricity price according
to peak and low periods of power consumption. TXU Energy utilized such price
lever to stabilize the peak and low fluctuations of power consumption. As a matter
of fact, the application of big data in the smart grid can help the realization
of time-sharing dynamic pricing, which is a win-win situation for both energy
suppliers and users.

￿ Access of Intermittent Renewable Energy : At present, many new energy

resources, such as wind energy and solar energy, are also accessed to power grids.
However, since the power generation capacities of such new energy resources are
closely related to climate conditions that feature randomness and intermittency,
it is challenging to access them to power grids. If the big data of power grids
is effectively analyzed, such intermittent renewable new energy sources can
be effectively managed: the electricity generated by the new energy resources
can be allocated to regions with electricity shortage. Such energy resources can
complement the traditional hydropower and thermal power generations.

References

1. Rita L Sallam, James Richardson, John Hagerty, and Bill Hostmann. Magic quadrant for
business intelligence platforms. Gartner Group, Stamford, CT , 2011.
2. Beyond the pc. Special Report on Personal TEchnology, 2011.
3. Stephen A Goff, Matthew Vaughn, Sheldon McKay, Eric Lyons, Ann E Stapleton, Damian
Gessler, Naim Matasci, Liya Wang, Matthew Hanlon, Andrew Lenards, et al. The iplant
collaborative: cyberinfrastructure for plant biology. Frontiers in plant science , 2, 2011.
4. D Agrawal, P Bernstein, E Bertino, S Davidson, U Dayal, M Franklin, J Gehrke, L Haas,
A Halevy, J Han, et al. Challenges and opportunities with big data. a community white paper
developed by leading researchers across the united states, 2012.
5. George K Baah, Alexander Gray, and Mary Jean Harrold. On-line anomaly detection of
deployed software: a statistical machine learning approach. In Proceedings of the 3rd inter-
national workshop on Software quality assurance , pages 70-77. ACM, 2006.
6. Michael Moeng and Rami Melhem. Applying statistical machine learning to multicore voltage
& frequency scaling. In Proceedings of the 7th ACM international conference on Computing
frontiers , pages 277-286. ACM, 2010.
7. Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy. Mining data streams:
areview. ACM Sigmod Record , 34(2):18-26, 2005.