Applications of Smart Grid Big Data Analytics

doi:10.3969/j.issn.1000-7229.2015.10.002

Abstract

Abstract:

With the rapid progress in information technology, a novel concept—“Energy Internet”, that concentrates on the coordination and optimization of multi-type energy flows via advanced communication and internet technology, has received a lot of attention. The result of such an inevitable trend is that a fundamental technique—Big Data Analytics—must be developed to handle massive influx of data from multiple heterogeneous sources, as well as utilize the data to swiftly derive an economic value. The paper gives an overview on research works conducted at SGRI North America big data lab with highlights on hardware configuration and software deployment of the cluster environment. The paper reviews several ongoing research topics performed in the lab with an emphasis on customer segmentation and response targeting （collaboration with Stanford University）；and energy disaggregation. These works are built on an integrated power system data model that is supported by open source technology. Preliminary results show that our research will benefit both utility companies and customers.

Key words: Big Data Analytics, mixed-integer programming, customer segmentation and targeting, energy disaggregation

CLC Number:

TM 76

GAO Feng,LIU Guangyi SAUNDERS Chris,ZHU Wendong,TAN Chin-woo, YU Yang. Applications of Smart Grid Big Data Analytics[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(10): 11-19.

References

［1］What is big data analytics?-Definition from WhatIs.com［EB/OL］. http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics.
［2］Smart electric meters, Advanced metering infrastructure, and meter communications: Global market analysis and forecasts［EB/OL］ Navigant Research, 2013. http://www.navigantresearch.com/research/smart-meters.
［3］Yu N, Shah S, Johnson R, et al. Big data analytics in power distribution systems［J］. IEEE ISGT 2015.
［4］Wikipedia［EB/OL］. https://en.wikipedia.org/wiki/Phasor_measurement_unit
［5］Xie L, Chen Y, Kumar P R. Dimensionality reduction of synchrophasor data for early anomaly detection: linearized analysis［J］. IEEE Transactions on Power Systems, 2014, 29（6）: 2784-2794.
［6］Liu G, Yu Y, Gao F,et al. Research of smart gird big data model［C］. 23rd International Conference on Electricity Distribution Lyon, 2015.
［7］Saunders C, Liu G, Yu Y, et al. Data-driven distributed analytics and control platform for smart grid situational awareness［C］. CSEE, 2015.
［8］Spark official website［EB/OL］. https://spark.apache.org/.
［9］Liu G, Zhu W, Saunders C, et al. Real-Time Complex Event Processing and Analytics for Smart Grid［C］//Complex Adaptive Systems, San Jose: Conference Organized by Missouri University of Science and Technology, 2015.
［10］Kwac J, Tan C, Sintov N, et al. Utility customer segmentation based on Smart Meter Data: Empirical Study［J］. IEEE Smart Grid Community, 2013.
［11］Kwac J, Rajagopal R, Demand response targeting using big data analytics［C］// 2013 IEEE International Conference on Big Data.
［12］Dong R, Ratliff L, Ohlsson H, et al. A dynamical systems approach to energy disaggregation［C］//2013 IEEE 52nd Annual Conference on Decision and Control （CDC）.
［13］Kolter J Z, Batra S, Ng A Y. Energy disaggregation via discriminative sparse coding［C］//Advances in Neural Information Processing Systems, 2010.
［14］Drenker S, Kader A. Nonintrusive monitoring of electric loads［J］. IEEE Computer Applications in Power, 1999, 12（4）: 47-51.
［15］Rahayu D, Narayanaswamy B, Krishnaswamy S. Learning to be energy-wise: discriminative methods for load disaggregation［C］//Third International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet （e-Energy）, 2012.
［16］Farinaccio L, Zmeureanu R. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses［J］. Energy and Buildings, 1999, 30（3）: 245-259.
［17］Rajan D, Takriti S. Minimum up/down polytopes of the unit commitment problem with start-up costs［R］.IBM Research Report, 2005.

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