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References

    1. 1)
      • 1. Trelewicz, J.Q.: ‘Big data and big money: the role of data in the financial sector’, IT Prof., 2017, 19, (3), pp. 810.
    2. 2)
      • 2. E. I. Lab: ‘Big data in banking for marketers how to derive value from big data’. White Paper, 2015.
    3. 3)
      • 3. Srinivasan, U., Arunasalam, B.: ‘Leveraging big data analytics to reduce healthcare costs’, IT Prof., 2013, 15, (6), pp. 2128.
    4. 4)
      • 4. Islam, M.M., Razzaque, M.A., Hassan, M.M., et al: ‘Mobile cloud-based big healthcare data processing in smart cities’, IEEE Access, 2017, 5, pp. 1188711899.
    5. 5)
      • 5. Marjani, M., Nasaruddin, F., Gani, A., et al: ‘Big IoT data analytics: architecture, opportunities, and open research challenges’, IEEE Access, 2017, 5, pp. 52475261.
    6. 6)
      • 6. Satyanarayanan, M., Simoens, P., Xiao, Y., et al: ‘Edge analytics in the internet of things’, IEEE Pervasive Comput., 2015, 14, (2), pp. 2431.
    7. 7)
      • 7. Sharma, S.K., Wang, X.: ‘Live data analytics with collaborative edge and cloud processing in wireless IoT networks’, IEEE Access, 2017, 5, pp. 46214635.
    8. 8)
      • 8. He, X., Ai, Q., Qiu, R.C., et al: ‘A big data architecture design for smart grids based on random matrix theory’, IEEE Trans. Smart Grid, 2017, 8, (2), pp. 674686.
    9. 9)
      • 9. Sun, Y., Song, H., Jara, A.J., et al: ‘Internet of things and big data analytics for smart and connected communities’, IEEE Access, 2016, 4, pp. 766773.
    10. 10)
      • 10. Ta-Shma, P., Akbar, A., Gerson-Golan, G., et al: ‘An ingestion and analytics architecture for IoT applied to smart city use cases’, IEEE Internet Things J., 2017, 5, pp. 765774.
    11. 11)
      • 11. Wedgwood, K., Howard, R.: ‘Big data and analytics in travel and transportation’. IBM Big Data and Analytics White Paper, 2014.
    12. 12)
      • 12. Hong, T.: ‘Big data analytics: making the smart grid smarter [Guest Editorial]’, IEEE Power Energy Mag., 2018, 16, (3), pp. 1216.
    13. 13)
      • 13. Bose, A.: ‘Smart transmission grid applications and their supporting infrastructure’, IEEE Trans. Smart Grid, 2010, 1, (1), pp. 1119.
    14. 14)
      • 14. Meliopoulos, A.P.S., Cokkinides, G., Huang, R., et al: ‘Smart grid technologies for autonomous operation and control’, IEEE Trans. Smart Grid, 2011, 2, (1), pp. 110.
    15. 15)
      • 15. Heydt, G.T.: ‘The next generation of power distribution systems’, IEEE Trans. Smart Grid, 2010, 1, (3), pp. 225235.
    16. 16)
      • 16. Hou, W., Ning, Z., Guo, L., et al: ‘Temporal, functional and spatial big data computing framework for large-scale smart grid’, IEEE Trans. Emerging Top. Comput., 2018, pp. 11, to appear.
    17. 17)
      • 17. Zhou, K., Fu, C., Yang, S.: ‘Big data driven smart energy management: from big data to big insights’, Renew. Sustain. Energy Rev., 2016, 56, pp. 215225.
    18. 18)
      • 18. Yu, N., Shah, S., Johnson, R., et al: ‘Big data analytics in power distribution systems’. Proc. IEEE Power Energy Society Innovative Smart Grid Technologies Conf. (ISGT), February 2015, pp. 15.
    19. 19)
      • 19. Kim, Y.J., Thottan, M., Kolesnikov, V., et al: ‘A secure decentralized data-centric information infrastructure for smart grid’, IEEE Commun. Mag., 2010, 48, (11), pp. 5865.
    20. 20)
      • 20. Garrity, T.F.: ‘Getting smart’, IEEE Power Energy Mag., 2008, 6, (2), pp. 3845.
    21. 21)
      • 21. Zinaman, O., Miller, M., Adil, A., et al: ‘Power systems of the future’, Electr. J., 2015, 28, (2), pp. 113126.
    22. 22)
      • 22. Dijcks, J.-P.: ‘Oracle: big data for the enterprise’. Oracle White Paper, 2012.
    23. 23)
      • 23. Stimmel, C.L.: ‘Big data analytics strategies for the smart grid’ (CRC Press, Boca Raton, 2014).
    24. 24)
      • 24. Chen, S., Wei, Z., Sun, G., et al: ‘Identifying optimal energy flow solvability in electricity-gas integrated energy systems’, IEEE Trans. Sustain. Energy, 2017, 8, (2), pp. 846854.
    25. 25)
      • 25. Callahan, S.: ‘Big data: the future of energy and utilities’. Available at https://www.rdmag.com/article/2015/10/big-data-future-energy-and-utilities, accessed 5 October 2015.
    26. 26)
      • 26. Dong, Z., Zhang, P.: ‘Emerging techniques in power system analysis’ (Springer, Berlin, 2010).
    27. 27)
      • 27. Hu, J., Vasilakos, A.V.: ‘Energy big data analytics and security: challenges and opportunities’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24232436.
    28. 28)
      • 28. Haase, P.: ‘Intelligrid: a smart network of power’, EPRI J., 2005, pp. 2632.
    29. 29)
      • 29. Mastelic, T., Oleksiak, A., Claussen, H., et al: ‘Cloud computing: survey on energy efficiency’, ACM Comput. Surv., 2015, 47, (2), p. 33.
    30. 30)
      • 30. Jiang, H., Wang, K., Wang, Y., et al: ‘Energy big data: a survey’, IEEE Access, 2016, 4, pp. 38443861.
    31. 31)
      • 31. Hebner, R.: ‘Nanogrids, microgrids, and big data: the future of the power grid’. Available at http://spectrum.ieee.org/energy/renewables/nanogrids-microgrids-and-bigdata-the-future-of-the-power-grid, accessed 31 March 2017.
    32. 32)
      • 32. SunGard: ‘Big data – challenges and opportunities for the energy industry’. White paper, 2013.
    33. 33)
      • 33. Asad, Z., Chaudhry, M.A.R.: ‘A two-way street: green big data processing for a greener smart grid’, IEEE Syst. J., 2017, 11, (2), pp. 784795.
    34. 34)
      • 34. SWECO: ‘Smart grid and big data analytics’. Technical report, 2015.
    35. 35)
      • 35. Pancholi, S.: ‘Solving big data challenges us electric utility industry’. PES General Meeting Presentation, 2014.
    36. 36)
      • 36. Johnson, J.R.: ‘How four U.S. utilities are tackling big data’, 2014. Available at http://www.energycentral.com/c/iu/how-four-us-utilities-are-tackling-big-data.
    37. 37)
      • 37. C. Consulting: ‘Big data blackout: are utilities powering up their data analytics?’. Technical report, 2015.
    38. 38)
      • 38. C. E. Association: ‘Electric utility innovation toward vision 2050’. Technical report, 2015.
    39. 39)
      • 39. Dong, H., Singh, G., Attri, A., et al: ‘Open data-set of seven Canadian cities’, IEEE Access, 2017, 5, pp. 529543.
    40. 40)
      • 40. Siemens: ‘EnergyIp – a flexible, scalable platform for MDM and more’. Available at http://w3.usa.siemens.com/smartgrid/us/en/smart-metering/energyip-mdmsplatform/pages/energyip.aspx.
    41. 41)
      • 41. Siemens: ‘Siemens EnergyIp application platform: maximize the return on your smart grid investment’. Available at http://w3.siemens.com/smartgrid/global/en/productssystems-solutions/smart-metering/emeter/pages/energyip-platform.aspx.
    42. 42)
      • 42. GE: ‘Predix: the industrial internet platform’. White paper, November 2016.
    43. 43)
      • 43. G. Electric: ‘The role of big data visualization and analytics in the utility industry’. Available at http://www.electricenergyonline.com/show_article.php?mag=92&article=750, accessed 2017.
    44. 44)
      • 44. GE: ‘Grid IQ insight: translating data to actionable intelligence for empowered decision making’. Available at https://www.gegridsolutions.com/uos/catalog/grid-iq-insight.htm, accessed 2014.
    45. 45)
      • 45. ABB: ‘Using smart grid data to power end-to-end asset management’. White paper, 2011.
    46. 46)
      • 46. Chavero, M.: ‘New smart asset management strategies in TSO industry enabled by a real-time data infrastructure’. White paper, 2016.
    47. 47)
      • 47. Xie, L., Chen, Y., Kumar, P.R.: ‘Dimensionality reduction of synchrophasor data for early event detection: linearized analysis’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 27842794.
    48. 48)
      • 48. Zhou, D., Guo, J., Zhang, Y., et al: ‘Distributed data analytics platform for wide-area synchrophasor measurement systems’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 23972405.
    49. 49)
      • 49. Zomaya, A.Y., Lee, Y.C.: ‘Energy efficient distributed computing systems’, vol. 88 (John Wiley & Sons, Hoboken, 2012).
    50. 50)
      • 50. Cheng, R., Kalashnikov, D.V., Prabhakar, S.: ‘Evaluating probabilistic queries over imprecise data’. Proc. SIGMOD Int. Conf. on Management of Data, 2003, pp. 551562.
    51. 51)
      • 51. Tsang, S., Kao, B., Yip, K.Y., et al: ‘Decision trees for uncertain data’, IEEE Trans. Knowl. Data Eng., 2011, 23, (1), pp. 6478.
    52. 52)
      • 52. Wagstaff, K.: ‘Machine learning that matters’, arXiv preprint arXiv:1206.4656, 2012.
    53. 53)
      • 53. Li, F., Luo, B., Liu, P.: ‘Secure information aggregation for smart grids using homomorphic encryption’. Proc. First IEEE Int. Conf. on Smart Grid Communications (SmartGridComm), 2010, pp. 327332.
    54. 54)
      • 54. Kalogridis, G., Efthymiou, C., Denic, S.Z., et al: ‘Privacy for smart meters: towards undetectable appliance load signatures’. Proc. First IEEE Int. Conf. on Smart Grid Communications (SmartGridComm), 2010, pp. 232237.
    55. 55)
      • 55. Rastogi, V., Nath, S.: ‘Differentially private aggregation of distributed time series with transformation and encryption’. Proc. SIGMOD Int. Conf. on Management of Data, 2010, pp. 735746.
    56. 56)
      • 56. Liu, H., Ning, H., Zhang, Y., et al: ‘Aggregated-proofs based privacy-preserving authentication for V2G networks in the smart grid’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 17221733.
    57. 57)
      • 57. Markovic, D.S., Zivkovic, D., Branovic, I., et al: ‘Smart power grid and cloud computing’, Renew. Sustain. Energy Rev., 2013, 24, pp. 566577.
    58. 58)
      • 58. Qi, J., Hahn, A., Lu, X., et al: ‘Cybersecurity for distributed energy resources and smart inverters’, IET Cyber-Phys. Syst., Theor. Appl., 2016, 1, (1), pp. 2839.
    59. 59)
      • 59. He, D., Kumar, N., Zeadally, S., et al: ‘Efficient and privacy-preserving data aggregation scheme for smart grid against internal adversaries’, IEEE Trans. Smart Grid, 2017, 8, pp. 24112419.
    60. 60)
      • 60. Sciacca, S.: ‘Big data and the need for improved time synchronization standards’. Available at http://m.csemag.com/articlepage/big-data-and-the-need-for-improved-timesynchronization-standards/8c0cd0612438905a2e12ab5fb7e4dad4.html, accessed 19 September 2012.
    61. 61)
      • 61. Zhao, J., Zhang, G., Das, K., et al: ‘Power system real-time monitoring by using PMU-based robust state estimation method’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 300309.
    62. 62)
      • 62. Tao, Y., Papadias, D.: ‘The MV3R-tree: a spatio-temporal access method for timestamp and interval queries’. Proc. Very Large Data Bases Conf. (VLDB), Rome, 11–14 September 2001.
    63. 63)
      • 63. Pfoser, D., Jensen, C.S., Theodoridis, Y., et al: ‘Novel approaches to the indexing of moving object trajectories’. Proc. Very Large Data Bases Conf. (VLDB), 2000, pp. 395406.
    64. 64)
      • 64. Tayeb, J., Ulusoy, Ö, Wolfson, O.: ‘A quadtree-based dynamic attribute indexing method’, Comput. J., 1998, 41, (3), pp. 185200.
    65. 65)
      • 65. Kothuri, R.K.V., Ravada, S., Abugov, D.: ‘Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data’. Proc. SIGMOD Int. Conf. on Management of Data, 2002, pp. 546557.
    66. 66)
      • 66. Kamel, I., Faloutsos, C.: ‘Hilbert R-tree: an improved R-tree using fractals’. Tech. Rep., 1993.
    67. 67)
      • 67. Yigit, M., Gungor, V.C., Baktir, S.: ‘Cloud computing for smart grid applications’, Comput. Netw., 2014, 70, pp. 312329.
    68. 68)
      • 68. Rusitschka, S., Eger, K., Gerdes, C.: ‘Smart grid data cloud: a model for utilizing cloud computing in the smart grid domain’. Proc. First Int. Conf. on Smart Grid Communications (SmartGridComm), 2010, pp. 483488.
    69. 69)
      • 69. Giani, A., Bitar, E., Garcia, M., et al: ‘Smart grid data integrity attacks’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 12441253.
    70. 70)
      • 70. Ruj, S., Pal, A.: ‘Analyzing cascading failures in smart grids under random and targeted attacks’. Proc. IEEE 28th Int. Conf. on Advanced Information Networking and Applications (AINA), 2014, pp. 226233.
    71. 71)
      • 71. McGranaghan, M., Houseman, D., Schmitt, L., et al: ‘Enabling the integrated grid: leveraging data to integrate distributed resources and customers’, IEEE Power Energy Mag., 2016, 14, (1), pp. 8393.
    72. 72)
      • 72. Leeds, D.J.: ‘The soft grid 2013–2020: big data & utility analytics for smart grid’ (GTM Research, Cary, USA, 2012).
    73. 73)
      • 73. Tonyali, S., Akkaya, K., Saputro, N., et al: ‘A reliable data aggregation mechanism with homomorphic encryption in smart grid AMI networks’. Proc. IEEE 13th Annual Consumer Communications & Networking Conf. (CCNC), 2016, pp. 550555.
    74. 74)
      • 74. Zhu, W., Guo, Q.: ‘Data security and encryption technology research on smart grid communication system’. Proc. IEEE Eighth Int. Conf. on Measuring Technology and Mechatronics Automation (ICMTMA), 2016, pp. 175178.
    75. 75)
      • 75. Bansal, S.K.: ‘Towards a semantic extract-transform-load (ETL) framework for big data integration’. Proc. IEEE Int. Congress on Big Data (BigData Congress), 2014, pp. 522529.
    76. 76)
      • 76. Wang, Y., Deng, Q., Liu, W., et al: ‘A data-centric storage approach for efficient query of large-scale smart grid’. Proc. IEEE Ninth Web Information Systems and Applications Conf. (WISA), 2012, pp. 193197.
    77. 77)
      • 77. Tahmassebpour, M.: ‘A new method for time-series big data effective storage’, IEEE Access, 2017, 5, pp. 1069410699.
    78. 78)
      • 78. Wang, Y., Chen, Q., Kang, C., et al: ‘Clustering of electricity consumption behavior dynamics toward big data applications’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24372447.
    79. 79)
      • 79. Huang, D., Zareipour, H., Rosehart, W.D., et al: ‘Data mining for electricity price classification and the application to demand-side management’, IEEE Trans. Smart Grid, 2012, 3, (2), pp. 808817.
    80. 80)
      • 80. Singh, S., Yassine, A.: ‘Mining energy consumption behavior patterns for households in smart grid’, IEEE Trans. Emerging Top. Comput., 2018, pp. 11, to appear.
    81. 81)
      • 81. Sheng, G., Hou, H., Jiang, X., et al: ‘A novel association rule mining method of big data for power transformers state parameters based on probabilistic graph model’, IEEE Trans. Smart Grid, 2018, 9, (2), pp. 695702.
    82. 82)
      • 82. Gu, B., Sheng, V.S.: ‘A robust regularization path algorithm for v -support vector classification’, IEEE Trans. Neural Netw. Learn. Syst., 2017, 28, (5), pp. 12411248.
    83. 83)
      • 83. Pignati, M., Zanni, L., Romano, P., et al: ‘Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation’, IEEE Trans. Power Deliv., 2017, 32, (1), pp. 381392.
    84. 84)
      • 84. Jiang, H., Dai, X., Gao, D.W., et al: ‘Spatial-temporal synchrophasor data characterization and analytics in smart grid fault detection, identification, and impact causal analysis’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 25252536.
    85. 85)
      • 85. Usman, M.U., Faruque, M.O.: ‘Validation of a PMU-based fault location identification method for smart distribution network with photovoltaics using real-time data’, IET Gener. Transm. Distrib., 2018, 12, (21), pp. 58245833.
    86. 86)
      • 86. Hosseini, Z.S., Mahoor, M., Khodaei, A.: ‘AMI-enabled distribution network line outage identification via multi-label SVM’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 54705472.
    87. 87)
      • 87. Ahmed, A., Awais, M., Naeem, M., et al: ‘Multiple power line outage detection in smart grids: probabilistic Bayesian approach’, IEEE Access, 2018, 6, pp. 1065010661.
    88. 88)
      • 88. Jiang, Y., Liu, C.-C., Diedesch, M., et al: ‘Outage management of distribution systems incorporating information from smart meters’, IEEE Trans. Power Syst., 2016, 31, (5), pp. 41444154.
    89. 89)
      • 89. Jokar, P., Arianpoo, N., Leung, V.C.: ‘Electricity theft detection in AMI using customers’ consumption patterns’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 216226.
    90. 90)
      • 90. Prostejovsky, A.M., Gehrke, O., Kosek, A.M., et al: ‘Distribution line parameter estimation under consideration of measurement tolerances’, IEEE Trans. Ind. Inf., 2016, 12, (2), pp. 726735.
    91. 91)
      • 91. Azzouz, M.A., El-Saadany, E.F.: ‘Multivariable grid admittance identification for impedance stabilization of active distribution networks’, IEEE Trans. Smart Grid, 2017, 8, (3), pp. 11161128.
    92. 92)
      • 92. Wenli, F., Xuemin, Z., Shengwei, M., et al: ‘Vulnerable transmission line identification using ISH theory in power grids’, IET Gener. Transm. Distrib., 2017, 12, (4), pp. 10141020.
    93. 93)
      • 93. Babakmehr, M., Simões, M.G., Wakin, M.B., et al: ‘Compressive sensing-based topology identification for smart grids’, IEEE Trans. Ind. Inf., 2016, 12, (2), pp. 532543.
    94. 94)
      • 94. Cavraro, G., Kekatos, V.: ‘Graph algorithms for topology identification using power grid probing’, arXiv preprint arXiv:1803.04506, 2018.
    95. 95)
      • 95. Pappu, S.J., Bhatt, N., Pasumarthy, R., et al: ‘Identifying topology of low voltage distribution networks based on smart meter data’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 51135122.
    96. 96)
      • 96. Weng, Y., Liao, Y., Rajagopal, R.: ‘Distributed energy resources topology identification via graphical modeling’, IEEE Trans. Power Syst., 2017, 32, (4), pp. 26822694.
    97. 97)
      • 97. Chaouch, M.: ‘Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 411419.
    98. 98)
      • 98. Shi, H., Xu, M., Li, R.: ‘Deep learning for household load forecasting – a novel pooling deep RNN’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 52715280.
    99. 99)
      • 99. Gulbinas, R., Khosrowpour, A., Taylor, J.: ‘Segmentation and classification of commercial building occupants by energy-use efficiency and predictability’, IEEE Trans. Smart Grid, 2015, 6, (3), pp. 14141424.
    100. 100)
      • 100. Naeem, A., Shabbir, A., Hassan, N.U., et al: ‘Understanding customer behavior in multi-tier demand response management program’, IEEE Access, 2015, 3, pp. 26132625.
    101. 101)
      • 101. He, D., Du, L., Yang, Y., et al: ‘Front-end electronic circuit topology analysis for model-driven classification and monitoring of appliance loads in smart buildings’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 22862293.
    102. 102)
      • 102. Wang, Y., Chen, Q., Kang, C., et al: ‘Sparse and redundant representation-based smart meter data mmm compression and pattern extraction’, IEEE Trans. Power Syst., 2017, 32, (3), pp. 21422151.
    103. 103)
      • 103. Sui, Z., Niedermeier, M., de meer, H.: ‘TAI: a threshold-based anonymous identification scheme for demand-response in smart grids’, IEEE Trans. Smart Grid, 2018, 9, (4), pp. 34963506.
    104. 104)
      • 104. Ukil, A., Zivanovic, R.: ‘Automated analysis of power systems disturbance records: smart grid big data perspective’. Proc. IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 2014, pp. 126131.
    105. 105)
      • 105. Zhang, R.: at‘Big data analytics for smart grid-forecast, predict for a smarter grid’.
    106. 106)
      • 106. Pandey, R., Dhoundiyal, M., Kumar, A.: ‘Correlation analysis of big data to support machine learning’. Proc. IEEE Fifth Int. Conf. on Communication Systems and Network Technologies (CSNT), 2015, pp. 996999.
    107. 107)
      • 107. Zhou, K.-l., Yang, S.-l., Shen, C.: ‘A review of electric load classification in smart grid environment’, Renew. Sustain. Energy Rev., 2013, 24, pp. 103110.
    108. 108)
      • 108. Macedo, M., Galo, J., De Almeida, L., et al: ‘Demand side management using artificial neural networks in a smart grid environment’, Renew. Sustain. Energy Rev., 2015, 41, pp. 128133.
    109. 109)
      • 109. Monti, A., Ponci, F.: ‘Power grids of the future: why smart means complex’. Proc. IEEE Complexity in Engineering, 2010, pp. 711.
    110. 110)
      • 110. Sancho-Asensio, A., Navarro, J., Arrieta-Salinas, I., et al: ‘Improving data partition schemes in smart grids via clustering data streams’, Expert Syst. Appl., 2014, 41, (13), pp. 58325842.
    111. 111)
      • 111. Tong, X., Kang, C., Xia, Q.: ‘Smart metering load data compression based on load feature identification’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24142422.
    112. 112)
      • 112. de Souza, J.C.S., Assis, T.M.L., Pal, B.C.: ‘Data compression in smart distribution systems via singular value decomposition’, IEEE Trans. Smart Grid, 2017, 8, (1), pp. 275284.
    113. 113)
      • 113. Martins, A.D., Gurjão, E.C.: ‘Processing of smart meters data based on random projections’. Proc. IEEE Innovative Smart Grid Technologies Latin America (ISGT LA), 2013, pp. 14.
    114. 114)
      • 114. Dahal, N., King, R.L., Madani, V.: ‘Online dimension reduction of synchrophasor data’. Proc. IEEE Transmission and Distribution Conf. and Exposition (T&D), 2012, pp. 17.
    115. 115)
      • 115. Xing, E.P., Ho, Q., Dai, W., et al: ‘Petuum: a new platform for distributed machine learning on big data’, IEEE Trans. Big Data, 2015, 1, (2), pp. 4967.
    116. 116)
      • 116. D'Elia, A., Viola, F., Montori, F., et al: ‘Impact of interdisciplinary research on planning, running, and managing electromobility as a smart grid extension’, IEEE Access, 2015, 3, pp. 22812305.
    117. 117)
      • 117. Mohamed, N., Lazarova-Molnar, S., Jawhar, I., et al: ‘Towards service-oriented middleware for fog and cloud integrated cyber physical systems’. Proc. IEEE 37th Int. Conf. on Distributed Computing Systems Workshops (ICDCSW), 2017, pp. 6774.
    118. 118)
      • 118. Nguyen, K.-K., Cheriet, M.: ‘Virtual edge-based smart community network management’, IEEE Internet Comput., 2016, 20, (6), pp. 3241.
    119. 119)
      • 119. Mallik, R., Sarda, N., Kargupta, H., et al: ‘Distributed data mining for sustainable smart grids’. Proc. ACM SustKDD, 2011, vol. 11, pp. 16.
    120. 120)
      • 120. Akusok, A., Björk, K.-M., Miche, Y., et al: ‘High-performance extreme learning machines: a complete toolbox for big data applications’, IEEE Access, 2015, 3, pp. 10111025.
    121. 121)
      • 121. Shvachko, K., Kuang, H., Radia, S., et al: ‘The Hadoop distributed file system’. Proc. IEEE 26th Symp. on Mass Storage Systems and Technologies (MSST), 2010, pp. 110.
    122. 122)
      • 122. Green, R.C., Wang, L., Alam, M.: ‘Applications and trends of high performance computing for electric power systems: focusing on smart grid’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 922931.
    123. 123)
      • 123. Bera, S., Misra, S., Rodrigues, J.J.: ‘Cloud computing applications for smart grid: a survey’, IEEE Trans. Parallel Distrib. Syst., 2015, 26, (5), pp. 14771494.
    124. 124)
      • 124. Lee, Y.C., Zomaya, A.Y.: ‘Energy conscious scheduling for distributed computing systems under different operating conditions’, IEEE Trans. Parallel Distrib. Syst., 2011, 22, (8), pp. 13741381.
    125. 125)
      • 125. Ghamkhari, M., Mohsenian-Rad, H.: ‘Energy and performance management of green data centers: a profit maximization approach’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 10171025.
    126. 126)
      • 126. Simmhan, Y., Aman, S., Kumbhare, A., et al: ‘Cloud-based software platform for big data analytics in smart grids’, Comput. Sci. Eng., 2013, 15, (4), pp. 3847.
    127. 127)
      • 127. Ma, F., Luo, X., Litvinov, E.: ‘Cloud computing for power system simulations at ISO new England – experiences and challenges’, IEEE Trans. Smart Grid, 2016, 7, (6), pp. 25962603.
    128. 128)
      • 128. Fang, B., Yin, X., Tan, Y., et al: ‘The contributions of cloud technologies to smart grid’, Renew. Sustain. Energy Rev., 2016, 59, pp. 13261331.
    129. 129)
      • 129. Harvey, C., Rosen, S., Ramsey, J., et al: ‘Computationally and statistically efficient model fitting techniques’, J. Stat. Comput. Simul., 2017, 87, (1), pp. 123137.
    130. 130)
      • 130. Khosravi, A., Nahavandi, S., Creighton, D.: ‘Quantifying uncertainties of neural network-based electricity price forecasts’, Appl. Energy, 2013, 112, pp. 120129.
    131. 131)
      • 131. Schütte, S., Scherfke, S., Tröschel, M.: ‘Mosaik: a framework for modular simulation of active components in smart grids’. Proc. IEEE First Int. Workshop on Smart Grid Modeling and Simulation (SGMS), 2011, pp. 5560.
    132. 132)
      • 132. Stewart, E.M., Kiliccote, S., Shand, C., et al: ‘Addressing the challenges for integrating micro-synchrophasor data with operational system applications’. Proc. IEEE PES General Meeting| Conf. & Exposition, 2014, pp. 15.
    133. 133)
      • 133. Liu, F., Tong, J., Mao, J., et al: ‘NIST cloud computing reference architecture’, NIST Spec. Publ., 2011, 500, (2011), p. 292.
    134. 134)
      • 134. Borodo, S.M., Shamsuddin, S.M., Hasan, S.: ‘Big data platforms and techniques’, Indonesian J. Electr. Eng. Comput. Sci., 2016, 1, (1), pp. 191200.
    135. 135)
      • 135. Mishra, S.: ‘Survey of big data architecture and framework from the industry’ (NIST Big data Public Working Group, Gaithersburg, USA, 2015).
    136. 136)
      • 136. Ferguson, M.: ‘Architecting a big data platform for analytics’. A Whitepaper prepared for IBM, 2012, vol. 30.
    137. 137)
      • 137. B. Data: ‘Analytics reference architecture’. An Oracle White Paper, September 2013.
    138. 138)
      • 138. Vaidya, M., Deshpande, S.: ‘Distributed data management in energy sector using Hadoop’. Proc. IEEE Bombay Section Symp. (IBSS), 2015, pp. 16.
    139. 139)
      • 139. Niu, Z., He, B., Liu, F.: ‘JouleMR: towards cost-effective and green-aware data processing frameworks’, IEEE Trans. Big Data, 2018, 4, (2), pp. 258272.
    140. 140)
      • 140. Pal, A., Agrawal, S.: ‘An experimental approach towards big data for analyzing memory utilization on a Hadoop cluster using HDFS and MapReduce’. Proc. IEEE First Int. Conf. on Networks & Soft Computing (ICNSC), 2014, pp. 442447.
    141. 141)
      • 141. Chintapalli, S., Dagit, D., Evans, B., et al: ‘Benchmarking streaming computation engines: Storm, Flink and Spark streaming’. Proc. IEEE Int. Parallel and Distributed Processing Symp. Workshops, 2016, pp. 17891792.
    142. 142)
      • 142. Apache: ‘Apache drill – schema-free SQL for Hadoop, NoSQL and cloud storage’. Available at http://drill.apache.org/, accessed 17 June 2017.
    143. 143)
      • 143. Hadoop: ‘What is Apache Hadoop’. Available at http://hadoop.apache.org/, accessed January 2019.
    144. 144)
      • 144. Singh, D., Reddy, C.K.: ‘A survey on platforms for big data analytics’, J. Big. Data., 2015, 2, (1), p. 8.
    145. 145)
      • 145. Apache. Available at http://https://flink.apache.org/, accessed January 2019.
    146. 146)
      • 146. Maharjan, S., Zhu, Q., Zhang, Y., et al: ‘Dependable demand response management in the smart grid: a Stackelberg game approach’, IEEE Trans. Smart Grid, 2013, 4, (1), pp. 120132.
    147. 147)
      • 147. Kwac, J., Rajagopal, R.: ‘Demand response targeting using big data analytics’. Proc. IEEE Int. Conf. on Big Data, 2013, pp. 683690.
    148. 148)
      • 148. Wang, Y., Chen, Q., Hong, T., et al: ‘Review of smart meter data analytics: applications, methodologies, and challenges’, IEEE Trans. Smart Grid, 2018, p. 1, to appear.
    149. 149)
      • 149. Fallah, S.N., Deo, R.C., Shojafar, M., et al: ‘Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions’, Energies, 2018, 11, (3), p. 596.
    150. 150)
      • 150. Tureczek, A., Nielsen, P.S., Madsen, H.: ‘Electricity consumption clustering using smart meter data’, Energies, 2018, 11, (4), p. 859.
    151. 151)
      • 151. Agarwal, A., Balance, J., Bhargava, B., et al: ‘Real time dynamics monitoring system (RTDMS) for use with synchrophasor technology in power systems’. Proc. IEEE Power and Energy Society General Meeting, July 2011, pp. 18.
    152. 152)
      • 152. Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: ‘Big data analytics for dynamic energy management in smart grids’, Big Data Res., 2015, 2, (3), pp. 94101.
    153. 153)
      • 153. Marinakis, V., Doukas, H., Tsapelas, J., et al: ‘From big data to smart energy services: an application for intelligent energy management’, Future Gener. Comput. Syst., 2018, pp. 115, to appear.
    154. 154)
      • 154. Cao, Y., Song, H., Kaiwartya, O., et al: ‘Mobile edge computing for big-data-enabled electric vehicle charging’, IEEE Commun. Mag., 2018, 56, (3), pp. 150156.
    155. 155)
      • 155. Yassine, A., Singh, S., Alamri, A.: ‘Mining human activity patterns from smart home big data for healthcare applications’, IEEE Access, 2017, 5, pp. 1313113141.
    156. 156)
      • 156. Simmhan, Y., Aman, S., Cao, B., et al: ‘An informatics approach to demand response optimization in smart grids’. Tech. Rep., City of Los Angeles Department, 2011.
    157. 157)
      • 157. Chelmis, C., Kolte, J., Prasanna, V.K.: ‘Big data analytics for demand response: clustering over space and time’. Proc. IEEE Int. Conf. on Big Data (Big Data), October 2015, pp. 22232232.
    158. 158)
      • 158. Jindal, A., Kumar, N., Singh, M.: ‘A unified framework for big data acquisition, storage, and analytics for demand response management in smart cities’, Future Gener. Comput. Syst., 2018, pp. 114, to appear.
    159. 159)
      • 159. Perez-Chacon, R., Luna-Romera, J.M., Troncoso, A., et al: ‘Big data analytics for discovering electricity consumption patterns in smart cities’, Energies, 2018, 11, (3), p. 983.
    160. 160)
      • 160. Sun, H., Wang, Z., Wang, J., et al: ‘Data-driven power outage detection by social sensors’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 25162524.
    161. 161)
      • 161. Chen, P.-C., Dokic, T., Kezunovic, M.: ‘The use of big data for outage management in distribution systems’. Proc. Int. Conf. on Electricity Distribution (CIRED) Workshop, 2014.
    162. 162)
      • 162. Kezunovic, M., Xie, L., Grijalva, S.: ‘The role of big data in improving power system operation and protection’. 2013 IREP Symp. Bulk Power System Dynamics and Control – IX Optimization, Security and Control of the Emerging Power Grid, August 2013, pp. 19.
    163. 163)
      • 163. Wang, B., Fang, B., Wang, Y., et al: ‘Power system transient stability assessment based on big data and the core vector machine’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 25612570.
    164. 164)
      • 164. Jiang, T., Mu, Y., Jia, H., et al: ‘A novel dominant mode estimation method for analyzing inter-area oscillation in China southern power grid’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 25492560.
    165. 165)
      • 165. Zhou, Y., Arghandeh, R., Spanos, C.J.: ‘Partial knowledge data-driven event detection for power distribution networks’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 51525162.
    166. 166)
      • 166. Moghaddass, R., Wang, J.: ‘A hierarchical framework for smart grid anomaly detection using large-scale smart meter data’, IEEE Trans. Smart Grid, 2018, 9, (6), pp. 58205830.
    167. 167)
      • 167. Capitanescu, F., Ramos, J.M., Panciatici, P., et al: ‘State-of-the-art, challenges, and future trends in security constrained optimal power flow’, Electr. Power Syst. Res., 2011, 81, (8), pp. 17311741.
    168. 168)
      • 168. Ardakani, A.J., Bouffard, F.: ‘Identification of umbrella constraints in DC-based security-constrained optimal power flow’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 39243934.
    169. 169)
      • 169. Peppanen, J., Reno, M.J., Broderick, R.J., et al: ‘Distribution system model calibration with big data from AMI and PV inverters’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24972506.
    170. 170)
      • 170. Shaker, H., Zareipour, H., Wood, D.: ‘Estimating power generation of invisible solar sites using publicly available data’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24562465.
    171. 171)
      • 171. Shaker, H., Zareipour, H., Wood, D.: ‘A data-driven approach for estimating the power generation of invisible solar sites’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24662476.
    172. 172)
      • 172. Zhang, X., Grijalva, S.: ‘A data-driven approach for detection and estimation of residential PV installations’, IEEE Trans. Smart Grid, 2016, 7, (5), pp. 24772485.
    173. 173)
      • 173. Chu, L., Qiu, R., He, X., et al: ‘Massive streaming PMU data modelling and analytics in smart grid state evaluation based on multiple high-dimensional covariance test’, IEEE Trans. Big Data, 2018, 4, (1), pp. 5564.
    174. 174)
      • 174. Ross, K.J., Hopkinson, K.M., Pachter, M.: ‘Using a distributed agent-based communication enabled special protection system to enhance smart grid security’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 12161224.
    175. 175)
      • 175. Touhiduzzaman, M., Hahn, A., Srivastava, A.: ‘A diversity-based substation cyber defense strategy utilizing coloring games’, arXiv preprint arXiv:1802.02618, 2018.
    176. 176)
      • 176. Vellaithurai, C., Srivastava, A., Zonouz, S., et al: ‘CPIndex: cyberphysical vulnerability assessment for power-grid infrastructures’, IEEE Trans. Smart Grid, 2015, 6, (2), pp. 566575.
    177. 177)
      • 177. Ahmed, A., Krishnan, V., Foroutan, S., et al: ‘Cyber physical security analytics for anomalies in transmission protection systems’. 2018 IEEE Industry Applications Society Annual Meeting (IAS), 2018, pp. 18.
    178. 178)
      • 178. Touhiduzzaman, M., Hahn, A., Srivastava, A.: ‘Arcades: analysis of risk from cyber attack against defensive strategies for power grid’, IET Cyber-Phys. Syst., Theor. Appl., 2018, 3, (3), pp. 119128.
    179. 179)
      • 179. Mayilvaganan, M., Sabitha, M.: ‘A cloud-based architecture for big-data analytics in smart grid: a proposal’. Proc. IEEE Int. Conf. on Computational Intelligence and Computing Research (ICCIC), 2013, pp. 14.
    180. 180)
      • 180. Pecan Street Dataport. Available at https://dataport.cloud/.
    181. 181)
      • 181. Chen, X.-W., Lin, X.: ‘Big data deep learning: challenges and perspectives’, IEEE Access, 2014, 2, pp. 514525.
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