{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T18:43:10Z","timestamp":1735584190098},"reference-count":125,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,8,13]],"date-time":"2018-08-13T00:00:00Z","timestamp":1534118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s42162-018-0007-5","type":"journal-article","created":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T05:11:10Z","timestamp":1532927470000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":216,"title":["Big data analytics in smart grids: a review"],"prefix":"10.1186","volume":"1","author":[{"given":"Yang","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5504-7848","authenticated-orcid":false,"given":"Tao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ettore Francesco","family":"Bompard","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,13]]},"reference":[{"issue":"5","key":"7_CR1","doi-asserted-by":"publisher","first-page":"2587","DOI":"10.1109\/TII.2016.2638322","volume":"13","author":"A Ahmad","year":"2017","unstructured":"Ahmad A, Javaid N, Guizani M, Alrajeh N, Khan ZA (Oct. 2017) An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Transactions on Industrial Informatics 13(5):2587\u20132596","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR2","doi-asserted-by":"publisher","unstructured":"Ahmed N, Levorato M, Li GP (2017) Residential consumer-centric demand side management. IEEE Transactions on Smart Grid. \n https:\/\/doi.org\/10.1109\/TSG.2017.2661991","DOI":"10.1109\/TSG.2017.2661991"},{"issue":"8","key":"7_CR3","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TNNLS.2015.2418739","volume":"27","author":"R Ak","year":"2016","unstructured":"Ak R, Fink O, Zio E (2016) Two machine learning approaches for short-term wind speed time-series prediction. IEEE Transactions on Neural Networks and Learning Systems 27(8):1734\u20131747","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Alam Mollah Rezaul, Kashem M. Muttaqi, Abdesselam Bouzerdoum. Evaluating the effectiveness of a machine learning approach based on response time and reliability for islanding detection of distributed generation. IET renewable power generation (volume: 11, Issue: 11, 2017)","DOI":"10.1049\/iet-rpg.2016.0987"},{"issue":"3","key":"7_CR5","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.1109\/TIA.2015.2511160","volume":"52","author":"WH Allen","year":"2016","unstructured":"Allen WH, Rubaai A, Chawla R (May-June 2016) Fuzzy neural network-based health monitoring for HVAC system variable-air-volume unit. IEEE Trans Ind Appl 52(3):2513\u20132524","journal-title":"IEEE Trans Ind Appl"},{"issue":"2","key":"7_CR6","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TII.2016.2528819","volume":"12","author":"R Al-Otaibi","year":"2016","unstructured":"Al-Otaibi R, Jin N, Member IEEE, Wilcox T, Flach P (April 2016) Feature construction and calibration for clustering daily load curves from smart-meter data. IEEE Transactions on Industrial Informatics 12(2):645\u2013654","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Andalib-Bin-Karim C, Liang X, Khan N, Zhang H (2017) Determine Q-V characteristics of grid-connected wind farms for voltage control using a data-driven analytics approach. IEEE Trans Ind Appl 53(5)","DOI":"10.1109\/TIA.2017.2716343"},{"issue":"2","key":"7_CR8","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TII.2016.2520396","volume":"12","author":"M Babakmehr","year":"2016","unstructured":"Babakmehr M, Sim\u00f5es MG, Wakin MB, Harirchi F (2016) Compressive sensing-based topology identification for smart grids. IEEE Transactions on Industrial Informatics 12(2):532\u2013543","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR9","volume-title":"24th Iranian Conference on Electrical Engineering (ICEE)","author":"A Bahmanyar","year":"2016","unstructured":"Bahmanyar A, Jamali S, Estebsari A, Pons E, Bompard E, Patti E, Acquaviva A (2016) Emerging smart meters in electrical distribution systems: opportunities and challenges. In: 24th Iranian Conference on Electrical Engineering (ICEE). Shiraz, Iran"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Baimel D, Tapuchi S, Baimel N (2016) Smart grid communication technologies- overview, research challenges and opportunities. International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 22-24 June 2016, Anacapri, Italy","DOI":"10.1109\/SPEEDAM.2016.7526014"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Balouji E, Salor O (2017) Classification of power quality events using deep learning on events images. 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), 19-20 April 2017, Shahrekord, Iran","DOI":"10.1109\/PRIA.2017.7983049"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Bauman K, Tuzhilin A, Zaczynski R (2017) Using social sensors for detecting emergency events: a case of power outages in the electrical utility industry. ACM Transactions on Management Information Systems 8(2\u20133)","DOI":"10.1145\/3052931"},{"key":"7_CR13","volume-title":"In ASCI International Workshop on Computing in Civil Engineering","author":"M Berges","year":"2009","unstructured":"Berges M, Goldman E, Matthews HS, Soibelman L (2009) Learning systems for electric comsumption of buildings. In: In ASCI International Workshop on Computing in Civil Engineering"},{"key":"7_CR14","unstructured":"Big Data analytics and energy consumption. (2016) Available: \n http:\/\/www.lavastorm.com\/blog\/2012\/04\/09\/big-data-analytics-and-energy-consumption\/"},{"key":"7_CR15","unstructured":"Bo P, Wan C, Dong S, Lin J, Song Y, Yi Z, Xiong J (2016) A Two-stage Pattern Recognition Method for Electric Customer Classification in Smart Grid. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, vol. 12"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1109\/TII.2015.2486379","volume":"12","author":"FAS Borges","year":"2016","unstructured":"Borges FAS, Fernandes RAS, Silva IN, Silva CBS (April 2016) Feature extraction and power quality disturbances classification using smart meters signals. IEEE Transactions on Industrial Informatics 12(2):824\u2013833","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR17","volume-title":"ComparativeModels for electrical load forecasting","author":"DW Bunn","year":"1985","unstructured":"Bunn DW, Farmer ED (1985) ComparativeModels for electrical load forecasting. Wiley, New York"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Cai Y, Huang T, Bompard E, Cao Y, Li Y (2017) Self-Sustainable Community of Electricity Prosumers in the Emerging Distribution System. IEEE Transactions on Smart Grid, vol 8, no. 5, pp. 2207\u20132216","DOI":"10.1109\/TSG.2016.2518241"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Cai Y, Chow M-Y (2009) Exploratory analysis of massive data for distribution fault diagnosis in smart grids. IEEE Conference on Power & Energy Society General Meeting, July","DOI":"10.1109\/PES.2009.5275689"},{"key":"7_CR20","unstructured":"CEN-CENELEC-ETSI Smart Grid Working Group Reference Architecture (2012) Reference architecture for the smart grid. Tech Rep"},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jii.2017.08.001","volume":"9","author":"Y Cheng","year":"2018","unstructured":"Cheng Y, Chen K, Sun H, Zhang Y, Tao F (2018) Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration 9:1\u201313","journal-title":"Journal of Industrial Information Integration"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Chunming T, Xi H, Shuai Z, Jiang F (2017) Big data issues in smart grid \u2013 a review. Renew Sust Energ Rev 79:1099\u20131107","DOI":"10.1016\/j.rser.2017.05.134"},{"key":"7_CR23","unstructured":"Claessens BJ, Vrancx P, Ruelens F (2016) Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load control. IEEE Transactions on Smart Grid"},{"issue":"2","key":"7_CR24","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TR.1985.5221966","volume":"34","author":"AG Colombo","year":"1985","unstructured":"Colombo AG, Costantini D, Jaarsma RJ (1985) Bayes nonparametric estimation of time-dependent failure rate. IEEE Trans Rel 34(2):109\u2013112","journal-title":"IEEE Trans Rel"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Cui Q, El-Arroudi K, Jo\u2019os G\u2019e (2017) An Effective Feature Extraction Method in Pattern Recognition Based High Impedance Fault Detection. In: 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP), San Antonio, 17-20 Sept. 2017","DOI":"10.1109\/ISAP.2017.8071380"},{"key":"7_CR26","first-page":"368","volume-title":"In Neurocomputing","author":"E De Santis","year":"2015","unstructured":"De Santis E, Livi L, Sadeghian A, Rizzi A (2015) Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification. In: In Neurocomputing, vol 170, pp 368\u2013383 ISSN 0925-2312"},{"key":"7_CR27","doi-asserted-by":"crossref","unstructured":"De Santis E, Rizzi A, Sadeghian A (2017) A Learning Intelligent System for Classification and Characterization of Localized Faults in Smart Grids. 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, 5-8 June 2017","DOI":"10.1109\/CEC.2017.7969631"},{"key":"7_CR28","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.dss.2018.01.011","volume":"107","author":"TL Di Zhua","year":"2018","unstructured":"Di Zhua TL, Zhang J (2018) Unsupervised tip-mining from customer reviews. Decis Support Syst 107:116\u2013124","journal-title":"Decis Support Syst"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Dimitrovska T, Rude\u017e U, Mihali\u010d R (2017) Fast contingency screening based on data mining. In: IEEE EUROCON International Conference on Smart Technologies, Ohrid, 6-8 July 2017","DOI":"10.1109\/EUROCON.2017.8011219"},{"key":"7_CR30","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.cosrev.2015.05.002","volume":"17","author":"CK Emani","year":"2015","unstructured":"Emani CK, Cullot N, Nicolle C (2015) Understandable Big Data: A survey. Computer Science Review 17:70\u201381","journal-title":"Computer Science Review"},{"key":"7_CR31","volume-title":"USA","author":"Executive Office of the President","year":"2013","unstructured":"Executive Office of the President (2013) Economic benefits of increasing electric grid resilience to weather outages. In: USA"},{"key":"7_CR32","first-page":"296","volume-title":"Energy and Buildings","author":"C Fan","year":"2018","unstructured":"Fan C, Xiao F, Li Z, Wang J (2018) Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review. In: Energy and Buildings, vol 159, pp 296\u2013308"},{"key":"7_CR33","unstructured":"Ferhat U\u00c7AR, \u00d6mer Faruk AL\u00c7\u0130N, Be\u015fir DANDIL, Fikret ATA (2016) Machine Learning based Power Quality Event Classification using Wavelet_Entropy and Basic Statisticsal Features. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, 29 Aug.-1 Sept.2016"},{"issue":"3","key":"7_CR34","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TII.2013.2248371","volume":"9","author":"D Ghosh","year":"2013","unstructured":"Ghosh D, Ghose T, Mohanta DK (Aug. 2013) Communication feasibility analysis for smart grid with phasor measurement units. IEEE Trans. Ind. Informat. 9(3):1486\u20131496","journal-title":"IEEE Trans. Ind. Informat."},{"issue":"1","key":"7_CR35","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1109\/TSG.2015.2428706","volume":"7","author":"JM Gillis","year":"2016","unstructured":"Gillis JM, Alshareef SM, Morsi WG (2016) Nonintrusive load monitoring using wavelet design and machine learning. IEEE Transactions on Smart Grid 7(1):320\u2013328","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"6","key":"7_CR36","doi-asserted-by":"publisher","first-page":"2648","DOI":"10.1109\/TSG.2016.2532885","volume":"8","author":"JM Gillis","year":"2017","unstructured":"Gillis JM, Morsi WG (Nov. 2017) Non-intrusive load monitoring using semi-supervised machine learning and wavelet design. IEEE Transactions on Smart Grid 8(6):2648\u20132655","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"6","key":"7_CR37","doi-asserted-by":"publisher","first-page":"3217","DOI":"10.1109\/TPWRS.2014.2377213","volume":"30","author":"R Granell","year":"2015","unstructured":"Granell R, Axon CJ, Wallom DCH (Nov. 2015) Impact of raw data temporal resolution using selected clustering methods on residential electricity load profiles. IEEE Trans Power Syst 30(6):3217\u20133224","journal-title":"IEEE Trans Power Syst"},{"key":"7_CR38","doi-asserted-by":"crossref","unstructured":"Guerrero JI, Monedero I, Biscarri F, Biscarri J, Mill\u00e1n R, Le\u00f3n C (2018) Non-technical losses reduction by improving the inspections accuracy in a power utility. IEEE Trans Power Syst, vol. 33, pp. 1209-1218","DOI":"10.1109\/TPWRS.2017.2721435"},{"issue":"4","key":"7_CR39","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/TII.2011.2166794","volume":"7","author":"V Gungor","year":"2011","unstructured":"Gungor V et al (Sep. 2011) Smart grid technologies communications technologies and standards. IEEE Trans Ind Informat 7(4):529\u2013539","journal-title":"IEEE Trans Ind Informat"},{"key":"7_CR40","doi-asserted-by":"crossref","unstructured":"G\u00fcnther WA, Rezazade Mehrizi MH, Huysman M, Feldberg F (2017) Debating big data: a literature review on realizing value from big data. J Strateg Inf Syst 26:191\u2013209","DOI":"10.1016\/j.jsis.2017.07.003"},{"issue":"1","key":"7_CR41","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1109\/TSG.2015.2409786","volume":"7","author":"S Haben","year":"2016","unstructured":"Haben S, Singleton C, Grindrod P (Jan. 2016) Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Transactions on Smart Grid 7(1):136\u2013144","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"4","key":"7_CR42","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1049\/iet-gtd.2016.0795","volume":"11","author":"F Hashemi","year":"2017","unstructured":"Hashemi F, Mohammadi M, Kargarian A (2017) Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency. IET Generation, Transmission & Distribution 11(4):891\u2013904","journal-title":"IET Generation, Transmission & Distribution"},{"key":"7_CR43","doi-asserted-by":"crossref","unstructured":"He C, Lin G, Mo W (2016) A method for transient stability assessment based on pattern recognition. International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), 19-22 Oct. 2016","DOI":"10.1109\/ICSGCE.2016.7876081"},{"issue":"4","key":"7_CR44","doi-asserted-by":"publisher","first-page":"4089","DOI":"10.1109\/TPWRS.2013.2266617","volume":"28","author":"M He","year":"2013","unstructured":"He M, Zhang J, Vittal V (Nov. 2013) Robust online dynamic security Assesment using adaptive ensemble decision-tree learning. IEEE Trans Power Syst 28(4):4089\u20134098","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"7_CR45","first-page":"812","volume":"8","author":"N Henao","year":"2017","unstructured":"Henao N, Agbossou K, Kelouwani S, Dub\u00e9 Y, Fournier M (2017) Approach in nonintrusive type I load monitoring using subtractive clustering. IEEE Transactions on Smart Grid 8(2):812\u2013821","journal-title":"IEEE Transactions on Smart Grid"},{"key":"7_CR46","doi-asserted-by":"crossref","unstructured":"Imran K, Joshua Zhexue H, Md Abdul Masud, Qingshan J (2016) Segmentation of factories on electricity consumption behaviors using load profile data. IEEE Access 4:8394\u20138406","DOI":"10.1109\/ACCESS.2016.2619898"},{"issue":"1","key":"7_CR47","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/TNNLS.2015.2404775","volume":"27","author":"MK Jena","year":"2016","unstructured":"Jena MK, Samantaray SR (2016) Data-mining-based intelligent differential relaying for transmission lines including UPFC and wind farms. IEEE Transactions on Neural Networks and Learning Systems 27(1):8\u201317","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"5","key":"7_CR48","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1109\/TSG.2016.2552229","volume":"7","author":"H Jiang","year":"2016","unstructured":"Jiang H, Dai X, Gao DW, Zhang JJ, Zhang Y, Muljadi E (Sept. 2016) Spatial-temporal Synchrophasor data characterization and analytics in smart grid fault detection identification and impact casual analysis. IEEE Transactions on Smart Grid 7(5):2525\u20132536","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"3","key":"7_CR49","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TII.2016.2543145","volume":"12","author":"A Jindal","year":"2016","unstructured":"Jindal A, Dua A, Kaur K, Singh M, Kumar N, Mishra S (2016) Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Transactions on Industrial Informatics 12(3):1005\u20131016","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"8","key":"7_CR50","first-page":"1829","volume":"35","author":"Q Jiye","year":"2015","unstructured":"Jiye Q, Zhixiang J, Mengjie S et al (2015) Scenario analysis and application research on big data in smart power distribution and consumption systems. Proceedings of the CSEE 35(8):1829\u20131836","journal-title":"Proceedings of the CSEE"},{"issue":"1","key":"7_CR51","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TSG.2015.2425222","volume":"7","author":"P Jokar","year":"2016","unstructured":"Jokar P, Arianpoo N, Leung VCM (2016) Electricity theft detection in AMI using customers\u2019 consumption patterns. IEEE Transactions on Smart Grid 7(1):216\u2013226","journal-title":"IEEE Transactions on Smart Grid"},{"key":"7_CR52","unstructured":"Kaisler S, Amnour F, Alberto J (2012) \u201cBig data: issues and challenges moving forward\u201d, 46th IEEE international conference on system science, Wailea, Maui, HI, USA, 7-10 Jan. 2013"},{"issue":"2","key":"7_CR53","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1109\/JSYST.2014.2380432","volume":"11","author":"S Kar","year":"2017","unstructured":"Kar S, Samantaray SR, Dadash Zadeh M (2017) Data-mining model based intelligent differential microgrid protection scheme. IEEE Syst J 11(2):1161\u20131169","journal-title":"IEEE Syst J"},{"key":"7_CR54","first-page":"1","volume-title":"Proc. IEEE Power Energy Soc. Gen. Meeting","author":"V Kekatos","year":"2014","unstructured":"Kekatos V, Giannakis GB, Baldick R (2014) Grid topology identification using electricity prices. In: Proc. IEEE Power Energy Soc. Gen. Meeting. National Harbor, MD, USA, pp 1\u20135"},{"issue":"2","key":"7_CR55","first-page":"287","volume":"35","author":"L Keyan","year":"2015","unstructured":"Keyan L, Wanxin S, Dongxia Z et al (2015) Big data application requirements and scenario analysis in smart distribution network. Proceedings of the CSEE 35(2):287\u2013293","journal-title":"Proceedings of the CSEE"},{"issue":"6","key":"7_CR56","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1109\/TII.2017.2730846","volume":"13","author":"M Khodayar","year":"2017","unstructured":"Khodayar M, Kaynak O, Khodayar ME (Dec. 2017) Rough deep neural architecture for short-term wind speed forecasting. IEEE Transactions on Industrial Informatics 13(6):2770\u20132779","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR57","doi-asserted-by":"publisher","unstructured":"Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid (Early Access) \n https:\/\/doi.org\/10.1109\/TSG.2017.2753802","DOI":"10.1109\/TSG.2017.2753802"},{"key":"7_CR58","doi-asserted-by":"crossref","unstructured":"Kong W, Dong ZY, Ma J, Hill DJ, Zhao J, Luo F (2018) An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Transactions on Smart Grid 9(4):3362\u20133372","DOI":"10.1109\/TSG.2016.2631238"},{"key":"7_CR59","unstructured":"Lee W, Fung G, Lam H, Chan F, Lucente M (2004) Exploration on load signatures. International Conference on Electrical Engineering (ICEE)"},{"issue":"4","key":"7_CR60","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/JSYST.2014.2334637","volume":"9","author":"D Li","year":"2015","unstructured":"Li D, Jayaweera SK (Dec. 2015) Machine-learning aided optimal customer decision for an interactive smart grid. IEEE Syst J 9(4):1529\u20131540","journal-title":"IEEE Syst J"},{"key":"7_CR61","doi-asserted-by":"crossref","unstructured":"Li R, Gu C, Li F, Shaddick G, Dale M (2015a) Development of low voltage network Templates_Part I_Substation clustering and classification. IEEE Trans Power Syst 30(6)","DOI":"10.1109\/TPWRS.2014.2371474"},{"key":"7_CR62","doi-asserted-by":"crossref","unstructured":"Li R, Gu C, Li F, Shaddick G, Dale M (2015b) Development of low voltage network templates\u2014part II_ peak load estimation by Clusterwise regression. IEEE Trans Power Syst 30(6)","DOI":"10.1109\/TPWRS.2014.2371477"},{"issue":"6","key":"7_CR63","doi-asserted-by":"publisher","first-page":"4473","DOI":"10.1109\/TPWRS.2016.2536781","volume":"31","author":"R Li","year":"2016","unstructured":"Li R, Li F, Smith ND (Nov. 2016b) Multi-resolution load profile clustering for smart metering data. IEEE Trans Power Syst 31(6):4473\u20134482","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"7_CR64","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1109\/TII.2016.2638319","volume":"13","author":"R Li","year":"2017","unstructured":"Li R, Li F, Smith ND (June 2017) Load characterization and low-order approximation for smart metering data in the spectral domain. IEEE Transactions on Industrial Informatics 13(3):976\u2013984","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"7_CR65","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TPWRS.2015.2438322","volume":"31","author":"S Li","year":"2016","unstructured":"Li S, Wang P, Goel L (May 2016a) A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection. IEEE Trans Power Syst 31(3):1788\u20131798","journal-title":"IEEE Trans Power Syst"},{"key":"7_CR66","first-page":"551","volume-title":"IEEE Transactions on Power Delivery","author":"J Liang","year":"2010","unstructured":"Liang Jian, Simon K. K. Ng, Gail Kendall, and John W. M. Cheng. Load Signature Study-Part I: Basic Concept Structure and Methodology. IEEE Transactions on Power Delivery ( 25: 2, 2010): 551\u2013560"},{"issue":"2","key":"7_CR67","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TPWRD.2009.2033800","volume":"25","author":"J Liang","year":"2010","unstructured":"Liang J, Ng SKK, Kendall G, Cheng JWM (2010b) Load signature study-part II: disaggregation framework simulation and applications. IEEE Transactions on Power Delivery 25(2):561\u2013569","journal-title":"IEEE Transactions on Power Delivery"},{"key":"7_CR68","doi-asserted-by":"crossref","unstructured":"Lim Ee-Peng, Jaideep Srivastava, Satya Prabhakar, James Richardson, Entity identification in database integration, in information sciences, 89:1\u20132, 1996;1\u201338, ISSN 0020-0255","DOI":"10.1016\/0020-0255(95)00185-9"},{"issue":"2","key":"7_CR69","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/TPWRS.2013.2283064","volume":"29","author":"C Liu","year":"2014","unstructured":"Liu C, Sun K, Rather ZH, Chen Z, Bak CL, Th\u00f8gersen P, Lund P (2014) A Systematic Approach for Dynamic Security Assessment. IEEE Transactions on Power Systems 29(2):717\u2013730","journal-title":"IEEE Transactions on Power Systems"},{"key":"7_CR70","doi-asserted-by":"crossref","unstructured":"Liu D, Zeng L, Li C, Ma K, Chen Y, Cao Y (2018) A distributed short- term load forecasting method based on local weather information. IEEE Syst J vol. 12, pp. 208-215","DOI":"10.1109\/JSYST.2016.2594208"},{"key":"7_CR71","doi-asserted-by":"crossref","unstructured":"Lv J, Pawlak M, Annakkage UD (2017a) Prediction of the transient stability boundary based on Nonparameteric additive modeling. IEEE Trans Power Syst 32(6):4362\u20134369","DOI":"10.1109\/TPWRS.2017.2669839"},{"key":"7_CR72","doi-asserted-by":"crossref","unstructured":"Lv Z, Song H, Basanta-Val P, Steed A (2017b) Analytics MJN-GBD State of the art, challenges and future research topics. IEEE Transactions on Industrial Informatics 13(4):1891\u20131899","DOI":"10.1109\/TII.2017.2650204"},{"key":"7_CR73","unstructured":"Madhumita P, Tajane S, Indi B (2016) Assessment of system vulnerability for smart grid applications. IEEE International Conference on Engineering and Technology (ICETECH)"},{"issue":"6","key":"7_CR74","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1109\/TSG.2017.2693394","volume":"8","author":"V Malbasa","year":"2017","unstructured":"Malbasa V, Zheng C, Chen P-C, Popovic T, Kezunovic M (Nov. 2017) Voltage stability prediction using active machine learning. IEEE Transactions on Smart Grid 8(6):3117\u20133124","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"5","key":"7_CR75","doi-asserted-by":"publisher","first-page":"2295","DOI":"10.1109\/TSG.2015.2487501","volume":"7","author":"DP Mishra","year":"2016","unstructured":"Mishra DP, Samantaray SR, Joos G (2016) A combined wavelet and data-mining based intelligent protection scheme for microgrid. IEEE Transactions on Smart Grid 7(5):2295\u20132304","journal-title":"IEEE Transactions on Smart Grid"},{"key":"7_CR76","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1016\/j.rser.2015.09.077","volume":"53","author":"A Moreno-Munoz","year":"2016","unstructured":"Moreno-Munoz A, Bellido-Outeirino FJ, Siano P, Gomez-Nieto MA (2016) Mobile social media for smart grids customer engagement: emerging trends and challenges. Renew Sust Energ Rev 53:1611\u20131616","journal-title":"Renew Sust Energ Rev"},{"key":"7_CR77","doi-asserted-by":"crossref","unstructured":"Munshi AA, Mohamed YA-RI (2018) Extracting and defining flexibility of residential electrical vehicle charging loads. IEEE Transactions on Industrial Informatics vol 14, pp. 448-461","DOI":"10.1109\/TII.2017.2724559"},{"issue":"3","key":"7_CR78","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ress.2004.01.014","volume":"86","author":"DNP Murthy","year":"2004","unstructured":"Murthy DNP, Bulmer M, Eccleston JA (Dec. 2004) Weibull model selection for reliability modeling. Rel Eng Syst Safety 86(3):257\u2013267","journal-title":"Rel Eng Syst Safety"},{"key":"7_CR79","doi-asserted-by":"crossref","unstructured":"Nazaripouya H, Wang B, Wang Y, Chu P, Pota HR, Gadh R (2016) Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method. In: 2016 IEEE\/PES Transmission and Distribution Conference and Exposition (T&D), Dallas","DOI":"10.1109\/TDC.2016.7519959"},{"issue":"1","key":"7_CR80","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TPWRS.2015.2390132","volume":"31","author":"D Ni","year":"2016","unstructured":"Ni D, Benoit C, Foggia G, B\u00e9sanger Y, Senior Member IEEE, Wurt F (Jan. 2016) Neural network-based model Design for Short-Term Load Forecast in distribution systems. IEEE Trans Power Syst 31(1):72\u201381","journal-title":"IEEE Trans Power Syst"},{"key":"7_CR81","doi-asserted-by":"crossref","unstructured":"Non-Cooperative Game Model Applied to an Advanced Metering Infrastructure for Non-Technical Loss Screening in Micro-Distribution Systems. IEEE Transactions on Smart Grid, vol. 5, no. 5, 2014: 2468\u20132469","DOI":"10.1109\/TSG.2014.2327809"},{"key":"7_CR82","doi-asserted-by":"crossref","unstructured":"Papadopoulos PN, Guo T, Milanovi\u0107 JV (2018) Probabilistic framework for online identification of dynamic behavior of power systems with renewable generation. IEEE Trans Power Syst , vol. 33, pp. 45-54","DOI":"10.1109\/TPWRS.2017.2688446"},{"key":"7_CR83","unstructured":"PR Newswire. (2014). \u201cWorld Loses $89.3 Billion to Electricity Theft Annually, $58.7 Billion in Emerging Markets.\u201d [Online]. Available: \n http:\/\/www.prnewswire.com\/news-releases\/world-loses-893-billion-to-electricity-theft-annually-587-billion-in-emerging-markets-300006515.html\n \n , Accessed on: Jul. 2015"},{"key":"7_CR84","unstructured":"Qiu J, Wang H, Lin D, He B, Zhao W, Wei X (2016) Nonparameteric Regression-based Failure Rate Model for Electric Power Equipment Using Lifecycle Data. In: 2016 IEEE\/PES Transmission and Distribution Conference and Exposition (T&D), Dallas"},{"key":"7_CR85","doi-asserted-by":"crossref","unstructured":"Reinhardt A, Reinhardt D (2016) Detecting Anomalous Electrical Appliance Behavior based on Motif Transition Likelihood Matrices. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, NSW, Australia, Sydney, NSW, Australia","DOI":"10.1109\/SmartGridComm.2016.7778840"},{"key":"7_CR86","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2018.01.060","volume":"286","author":"A Roya","year":"2018","unstructured":"Roya A, Cruz a RMO, Sabourina R, Cavalcanti GDC (2018) A study on combining dynamic selection and data preprocessing for imbalance learning. Neurocomputing 286:179\u2013192","journal-title":"Neurocomputing"},{"key":"7_CR87","doi-asserted-by":"crossref","unstructured":"Sagiroglu S, Terzi R, Canbay Y, Colak I (2016) Big Data Issues in Smart Grid Systems. In: 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, pp 20\u201323","DOI":"10.1109\/ICRERA.2016.7884486"},{"key":"7_CR88","doi-asserted-by":"crossref","unstructured":"Shah Z, Anwar A, Mahmood AN, Tari Z, Zomaya AY (2017) A Spatio-temporal Data Summarization Paradigm for Real-time Operation of Smart Grid. IEEE Transactions on Big Data PP(99)","DOI":"10.1109\/TBDATA.2017.2691350"},{"key":"7_CR89","doi-asserted-by":"crossref","unstructured":"Sheng G, Hou H, Jiang X, Chen Y (2018) A novel association rule mining method of big data for power transformer state parameters based on probabilistic graph model. IEEE Transactions on Smart Grid, vol. 9, pp. 695-702","DOI":"10.1109\/TSG.2016.2562123"},{"key":"7_CR90","doi-asserted-by":"publisher","unstructured":"Shi H, Xu M, Li R (2017) Deep learning for household load forecasting \u2013 a novel pooling deep RNN. IEEE Transactions on Smart Grid, \n https:\/\/doi.org\/10.1109\/TSG.2017.2686012","DOI":"10.1109\/TSG.2017.2686012"},{"key":"7_CR91","doi-asserted-by":"crossref","unstructured":"Singh S, Yassine A (2017) Mining energy consumption behavior patterns for households in smart grid. IEEE Transactions on Emerging Topics in Computing","DOI":"10.1109\/TETC.2017.2692098"},{"key":"7_CR92","doi-asserted-by":"crossref","unstructured":"Singh S, Majumdar A (2017) Deep Sparse Coding for Non-intrusive Load Monitoring. IEEE Transactions on Smart Grid","DOI":"10.1109\/TSG.2017.2666220"},{"key":"7_CR93","first-page":"1056","volume":"4","author":"J Siryani","year":"2017","unstructured":"Siryani J, Tanju B, Eveleighi TJ (2017) A machine learning decision-support system improves the internet of things\u2019 smart meter operations. Accident Analysis and Prediction, volume 4:1056\u20131066","journal-title":"Accident Analysis and Prediction, volume"},{"key":"7_CR94","unstructured":"SmartGrids European Tech. Platform, Strategic Deployment Document for Europe\u2019s Electricity Networks of the Future 6 (2010) [hereinafter E.U. SmartGrids SDD]"},{"key":"7_CR95","doi-asserted-by":"crossref","unstructured":"Sultanem F (1991) Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transaction on Power Delivery 6(4)","DOI":"10.1109\/61.97667"},{"issue":"5","key":"7_CR96","doi-asserted-by":"publisher","first-page":"2516","DOI":"10.1109\/TSG.2016.2546181","volume":"7","author":"H Sun","year":"2016","unstructured":"Sun H, Wang Z, Wang J, Huang Z, Carrington NL, Liao J (2016) Data-driven power outage detection by social sensors. IEEE Transactions on Smart Grid 7(5):2516\u20132524","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"2","key":"7_CR97","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1049\/iet-smt.2015.0169","volume":"10","author":"A Swetapadma","year":"2016","unstructured":"Swetapadma A, Yadav A (2016) Data-mining-based fault during power swing identification in power transmission system. IET Science, Measurement & Technology 10(2):130\u2013139","journal-title":"IET Science, Measurement & Technology"},{"key":"7_CR98","doi-asserted-by":"crossref","unstructured":"Tang Y, Ten C-W, Wang C, Parker G (2015) Extraction of energy information from analog meters using image processing. IEEE Transactions on Smart Grid 6(4)","DOI":"10.1109\/TSG.2015.2388586"},{"issue":"12","key":"7_CR99","first-page":"3305","volume":"38","author":"Z Teng","year":"2014","unstructured":"Teng Z, Yan Z, Dongxia Z (2014) Application Technology of big Data in smart distribution grid and its Prospect analysis. Power System Technology 38(12):3305\u20133312","journal-title":"Power System Technology"},{"issue":"5","key":"7_CR100","doi-asserted-by":"publisher","first-page":"2414","DOI":"10.1109\/TSG.2016.2544883","volume":"7","author":"X Tong","year":"2016","unstructured":"Tong X, Kang C, Xia Q (2016) Smart metering load data compression based on load feature identification. IEEE Transactions on Smart Grid 7(5):2414\u20132422","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"4","key":"7_CR101","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/TPWRS.2006.881133","volume":"21","author":"SV Verd\u00fa","year":"2006","unstructured":"Verd\u00fa SV, Garc\u00eda MO, Senabre C, Mar\u00edn AG, Franco FJG (2006) Classification filtering and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Trans Power Syst 21(4):1672\u20131682","journal-title":"IEEE Trans Power Syst"},{"issue":"5","key":"7_CR102","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1109\/TSG.2016.2549063","volume":"7","author":"B Wang","year":"2016","unstructured":"Wang B, Member BF, Wang Y, Liu H, Liu Y (2016b) Power system transient stability assessment based on big data and the Core vector machine. IEEE Transactions on Smart Grid 7(5):2561\u20132570","journal-title":"IEEE Transactions on Smart Grid"},{"key":"7_CR103","doi-asserted-by":"crossref","unstructured":"Wang Jian, Xiaofu Xiong, Ning Zhou, Zhe Li, Wei Wang. Early warning method for transmission line galloping based on SVM and AdaBoost bi-level classifiers. IET Generation, Transmission & Distribution (Volume: 10, Issue: 14, 11 4 2016a): 3499\u20133507","DOI":"10.1049\/iet-gtd.2016.0140"},{"key":"7_CR104","doi-asserted-by":"crossref","unstructured":"Wang X, McArthur S, Strachan S, Kirkwood J, Paisley B (2017a) A data analytic approach fault diagnosis and prognosis for distribution automation. IEEE Transactions on Smart Grid","DOI":"10.1109\/TSG.2017.2707107"},{"issue":"3","key":"7_CR105","doi-asserted-by":"publisher","first-page":"2142","DOI":"10.1109\/TPWRS.2016.2604389","volume":"32","author":"Y Wang","year":"2017","unstructured":"Wang Y, Chen Q, Kang C, Xia Q, Luo M (2017b) Sparse and redundant representation-based smart meter data compression and pattern extraction. IEEE Trans Power Syst 32(3):2142\u20132151","journal-title":"IEEE Trans Power Syst"},{"key":"7_CR106","unstructured":"Wei L, Zhang D, Wang X, Liu D, Wu Q. Power System Transient Stability Analysis Based on Random Matrix Theory. Proceedings of the CSEE,36 ;18 pp: 4854\u20134863, 2016"},{"issue":"4","key":"7_CR107","first-page":"2327","volume":"4","author":"W Wenbin","year":"2017","unstructured":"Wenbin W, Peng M (2017) A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-term Wind Power Forecasting. IEEE Internet of Things Journal 4(4):2327\u20134662","journal-title":"IEEE Internet of Things Journal"},{"key":"7_CR108","doi-asserted-by":"crossref","unstructured":"Wenhao P, Zhe D, Yanping Z, Jun L (2016) An analytical method for intelligent electricity use pattern with demand response. In: 2016 China International Conference on Electricity Distribution (CICED), Xi\u2019an","DOI":"10.1109\/CICED.2016.7576062"},{"key":"7_CR109","unstructured":"Xu X, He X, Ai Q, Qiu Caiming. A Correlation Analysis Method for Operation Status of Distribution Network Based on Random Matrix Theory. Power System Technology, Vol. 40 No. 3, pp: 781\u2013790, 2016"},{"key":"7_CR110","doi-asserted-by":"crossref","unstructured":"Yang M, Lin Y, Han X (2015) Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster-Shafer Theory. In: 2015 IEEE Industry Applications Society Annual Meeting, Addison","DOI":"10.1109\/IAS.2015.7356798"},{"issue":"8","key":"7_CR111","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1109\/TNNLS.2014.2351391","volume":"27","author":"R Ye","year":"2016","unstructured":"Ye R, Suganthan PN, Srikanth N (2016) A novel empirical mode decomposition with support vector regression for wind speed forecasting. IEEE Transactions on Neural Networks and Learning Systems 27(8):1793\u20131798","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"7_CR112","first-page":"14","volume":"9","author":"C Yu","year":"2002","unstructured":"Yu C (2002) Pan Heping. Business intelligence and its key technology Application Research of Computers 9:14\u201316","journal-title":"Business intelligence and its key technology Application Research of Computers"},{"key":"7_CR113","unstructured":"Zanetti M, Jamhour E, Pellenz M, Penna M, Zambenedetti V, Chueiri I (2017) A tunable fraud detection system for advanced metering infrastructure using short-lived patterns. IEEE Transactions on Smart Grid"},{"issue":"4","key":"7_CR114","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1049\/iet-gtd.2015.0003","volume":"10","author":"T-S Zhan","year":"2016","unstructured":"Zhan T-S, Chen S-J, Kao C-C, Kuo C-L, Chen J-L, Lin C-H (2016) Non-technical loss and power blackout detection under advanced metering infrastructure using a cooperative game based inference mechanism. IET Gener Transm Distrib 10(4):873\u2013882","journal-title":"IET Gener Transm Distrib"},{"issue":"4","key":"7_CR115","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TSG.2016.2552169","volume":"7","author":"D Zhang","year":"2016","unstructured":"Zhang D, Li S, Sun M, O\u2019Neill Z (July 2016b) An optimal and learning-based demand response and home energy management system. IEEE Transactions on Smart Grid 7(4):1790\u20131801","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"2","key":"7_CR116","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1109\/TII.2015.2492861","volume":"12","author":"J Zhang","year":"2016","unstructured":"Zhang J, Chung CY, Wang Z, Zheng X (April 2016a) Instantaneous electromechanical dynamics monitoring in smart transmission grid. IEEE Transactions on Industrial Informatics 12(2):844\u2013852","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"5","key":"7_CR117","doi-asserted-by":"publisher","first-page":"2544","DOI":"10.1109\/TII.2017.2676879","volume":"13","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Yan X, Dong ZY, Zhao X, Wong KP (Oct. 2017) Intelligent early warning of power system dynamic insecurity Risk_Toward optimal accuracy-earliness tradeoff. IEEE Transactions on Industrial Informatics 13(5):2544\u20132554","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR118","volume-title":"Similar Desires, Different Approaches. Public Utilities Fortnightly","author":"Z Zhang","year":"2011","unstructured":"Zhang Zhen. Smart Grid in America and Europe: Similar Desires, Different Approaches. Public Utilities Fortnightly, 149, 1, 2011"},{"issue":"1","key":"7_CR119","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1109\/TSG.2015.2431693","volume":"7","author":"J Zhao","year":"2016","unstructured":"Zhao J, Zhang G, Das K, Korres GN, Manousakis NM, Sinha AK, He Z (2016) Power System Real-Time Monitoring by Using PMU-based Robust State Estimation Method. IEEE Transactions on Smart Grid 7(1):300\u2013309","journal-title":"IEEE Transactions on Smart Grid"},{"key":"7_CR120","doi-asserted-by":"crossref","unstructured":"Zhao T, Ziqiang Z, Yan Z, Ping L, Yingjie T. Spatio-temporal analysis and forecasting of distributed PV systems Diffusion_a Case study of shanghai using A data-driven approach. IEEE Access 5: 5135\u20135148, 2017","DOI":"10.1109\/ACCESS.2017.2694009"},{"issue":"2","key":"7_CR121","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1109\/TPWRS.2011.2170592","volume":"27","author":"S Zhong","year":"2012","unstructured":"Zhong S, Tam K-S (May 2012) A frequency domain approach to characterize and Anlyze load profiles. IEEE Trans Power Syst 27(2):857\u2013865","journal-title":"IEEE Trans Power Syst"},{"issue":"5","key":"7_CR122","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1109\/TII.2017.2696534","volume":"13","author":"L Zhu","year":"2017","unstructured":"Zhu L, Chao L, Dong ZY, Hong C (2017) Imbalance learning machine-based power system short-term voltage stability assessment. IEEE Transactions on Industrial Informatics 13(5):2533\u20132543","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"7_CR123","doi-asserted-by":"crossref","unstructured":"Zhu Ting, Sheng Xiao, Qingquan Zhang, Yu Gu, Ping Yi, and Yanhua Li, \u201cEmergent Technologies in big Data Sensing: a survey\u201d, International Journal of Distributed Sensor Networks, Volume 2015, Article ID 902982","DOI":"10.1155\/2015\/902982"},{"key":"7_CR124","unstructured":"Zico Kolter J, Batra S, Ng AY (2010) Energy Disaggregation via Discriminative Sparse Coding. In: NIPS\u201910 Proceedings of the 23rd International Conference on Neural Information Processing Systems, vol 1, Vancouver, pp 1153\u20131161"},{"key":"7_CR125","volume-title":"Understanding big data: analytics for Enterprise class Hadoop and streaming data, McGraw-hill Education","author":"P Zikopoulos","year":"2011","unstructured":"Zikopoulos P, C. Eaton, Understanding big data: analytics for Enterprise class Hadoop and streaming data, McGraw-hill Education, 2011"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-018-0007-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s42162-018-0007-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-018-0007-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T19:08:34Z","timestamp":1565636914000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-018-0007-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,13]]},"references-count":125,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["7"],"URL":"https:\/\/doi.org\/10.1186\/s42162-018-0007-5","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,13]]},"assertion":[{"value":"27 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"8"}}