Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.


  • Keywords

    Big Data Issues; Smart Grids; Dynamic Energy Management; Performance; Load Classification; Distributed Systems.

  • References

      [1] S. F. Bush, S. Goel, G. Simard, IEEE vision for smart grid communicatins: 2030 andbeyond roadmap, IEEE std. Association (2013)1-19.

      [2] P.Goncalves Da Ailva, D.Ilic, S. Kamouskos, The impact of samrt grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading,IEEETrans.SmartGrid5(1)(2014)402-410.

      [3] Louie H.BurnsM.Lima C, An intoduction and users guide to the IEEE smart grid webportal, IEEE PES innovative smart grid technologies conference Europe (ISGT Europe):2010.p.1-5.

      [4] R. Mallik, N.Sarda, K.kargupta,S. Bandyopadhyay, distributed data mining for sustainable samrt grids, in: Proc. of ACM Sust KDD'11,2011,pp.1-6.

      [5] N.Balac,Greenmachine "intelligence: Greening and Sustaining smart grids, IEEE intell.Syst.28 (5)(2013) 50-55"

      [6] E.Ancillotti,R. Bruno, M.conti, The role of communication systems in smart grids:Architectures, technical solutions and research challenges, Comput. Commun. 36(17- 18)(2013) 1665-1697.

      [7] Z.Fan, Q.Chen,G.Kalogridis, S. Tan, D.KAleshi, The power of data: data analytics for M2M and smart grid,in: Proc. 3rdIEEE PES International Conference and Exhibition on innovative smart Grid technologies(ISGT Europe),2012,pp.1-8.

      [8] P.Mirowski, S.Chen, T.K. Ho, C,N Yu, Demand forcecasting in smart grids, Bell Labs Tech J.18(4)(2014) 135-158.

      [9] K.le Zhou, S.linYang,C.Shen, A review of electric load classification in smart grid environment, Renew. Sustai. Energy Rev.24(0)(2013)103-110.

      [10] Z.Vale, H.Morais,S.Ramos,J.Soares, P.Faria, Using datamining techniques to support DR programs definition in smartgrids, in Proc.IEEE Power and Energy Society General Meeting. 2011,pp.1-8.

      [11] A.UKil, R.Zivanovic, Automated analysis of power systems disturbance records: Smart grid big data perpective, in: Proc.IEEEInnovatic Smart Grid Technolgies-Asia (ISGT Asia), 2014,2014,pp.126-131.

      [12] Y.Simmhan,S.Aman,A.Kumbhare, R.Liu,S.Stevens, Q.Zhou,V.Prasanna,Cloud-based software platform for Big data analytics in smart grids, Comp.Sci.Eng,15(4)(2013)38-47.

      [13] D.J.Leeds, The soft grid 2013-2020: Big data & utility analytics for smartgrid,,Accessed:2014.12.06(2012)

      [14] C.L.Stimmel, Big Data Analytics Strategies for the SmartGrid, CRC Press,2014.7

      [15] T. Nguyen, V.Nunayath, A.Prinz, Big data Metadata Management in Smart Grids, in: Big Data and Internet of Things: A Roadmap for Smart Environments, Vol.546 of Studies in computational Intelligence, Springer International Publishing.2014, pp.189-214.

      [16] I.S.Group, managing big data for smart grids and smart meters, IBMcoporation, whitepaper (May2012).

      [17] M.Manfren, Multi-commodity network models for dynamic energy management- mathematical formulation, Energy Procedia 14(0) (2012)1380-1385.

      [18] P.Samadi, H.Mohsenian-Rad, V.W.S Wong, R.Schober, Real-time pricing for demand response based on stochastic approximation,IEEEtrans.Smart grid(2)(2014)789-798.

      [19] A-H, Mohsenian-Rad, V.W.S.Wong, J.Jatskevich, R.Schober, A.Leon- Garcia, Autonomous demand-side management based on game- theoretic energy consumption scheduling for the future smart grid, IEEE Trans. Smart Grid 1(3)(2010)320-331.

      [20] P.Siano, Demand respponse and smart grids A survey, Renew Sustain. Energy Rev.30(0)(2014) 461-478.

      [21] Khrennikov Yu A New intelLectual Networks (Smart Grid) for detectingelectrical equipment faults, defects and weakness, SmartGrid Renew Energy2012; 3:159-64.

      [22] S.C.Chan, K.M.Tsui, H.C.Wu,Y.Hou, Y-C. Wu, F.FWu, Load/price forecastingand managing demand response for smart grids:Methodologies and challenges, IEEE signal Process Mag 29(5)(2012)68-85

      [23] A.H.Mohsenian-Rad, A.Leon-Garcia, optimal residential load control with price prediction in real-time electricity pricing environment, IEEE trans Smart Grid 1(2)(2010)120- 133.

      [24] G.carpinelli, G.Celli, S.Mocci, F.Mottola, F.Pilo, D.Proto. Optimal integration of distributed energy storage devices in smart grids, IEEE Trans. Smart Grid 4(2)(2013) 985- 995.

      [25] A.Y.Saber, G.K.Venayagamoorthy, resource scheduling under uncertainty in a smart grid with renewable and plug-in vehicles, IEEE Syst. J.6 (1)(2012) 103-109.

      [26] F. Avila, D. Saez, G. Jimenez-Estevez,L. Reyes, A. Nunez Fuzzy demand forecasting in a predictive control strategy for renewable-energy based micro grid, in: Proc. European Control Conference(ECC), 2013,pp.2020{2025.

      [27] S. Balantrapu, Load forecasting in smart grid,

      [28] A. Motamedi, H. Zareipour, W. D. Rosehart, Electricity price and demand forecasting in smart grids, IEEE Trans. Smart Grid3 (2) (2012)664{674.

      [29] J. Pitt, A. Bourazeri, A. Nowak, M. Roszczynska-Kurasinska,A.Rychwalska, I. Santiago,M. Sanchez,M.Florea,M. Sanduleac.Transforming big data into collective awareness, Computer46(6) (2013)40{45.

      [30] J. DongLi, M. Xiaoli, S. Xioohui Study on technology system of self- healing control in smart Distribution grid, in: Proc. International Conference on Advanced Power System Automation and Protection (APAP), Vol. 1, 2011, pp.26{30.

      [31] Z. Aung, M. Toukhy, j. Williams, A. Sanchez, S. Herrero, Towards accurate electricity load Forecasting smart grids, in:Proc. 4th International Conference on Advances in Databases, Knowledge, and Data Applications(DBKDA),2012,PP.51{57.

      [32] P. Mack, Chapter 35- big data, data mining, and predictive analytics and high performance Computing, in: L. E. Jones (Ed.), Renewable Energy Integration, Academic Press, Boston, 2014,pp. 439 - 454.

      [33] J. Baek, Q. Vu, J, Liu, X. Huang,Y. Xiang, A secure cloud computing based framework for big Data information management of smart grid, IEEE Trans. CloudComput.PP(99) (2014) 1-1.

      [34] A. Dieb Martins, E. Gurjao, Processing of smart meters database on random projections, in: Proc.IEEEPES Conference on Innovative Smart Grid Technologies Latin America (ISGTLA), 2013, PP. 1-4.

      [35] K. Bhaduri, H. Kargupta, An e_cient local algorithm for distributed multivariate regression in Peer-to-peernetworks,in:SIAM Conference on Data Mining (SDM), 2008,pp.153-164.

      [36] R. C. Green, L. Wang, M. Alam, Applications and trends of high performance computing for Electric power systems: Focusing on smart grid, IEEE Trans.Smart Grid, 4(2) (2013)922-931.

      [37] M. Ali, Z. Y. Dong, X. Li, P. Zhang, RSA-grid: A grid computing based framework for power System reliability and security analysis,n: IEEE Power Engineering Society General Meeting, 2006, pp. 1-7.

      [38] WilliamsB.GahaganM,CostinK,UsingmicrogridstointegratedistributedRenewablesintothegrid,IEEEPESinnovativesmartgridtechnologiesconference Europe (ISGTEEurope);2010.p.1-5.

      [39] M. Yigit, V. C. Gungor, S. Baktir, Cloud computing for smart grid applications, Computer Networks 70 (0) (2014) 312{329.

      [40] A. Kasper, Legal aspects of cybersecurity in emerging technologies: smart grids and big data,in: T. Kerkme (Ed.), Regulating e technologies in the European Union, Springer InternationalPublishing 2014, pp.189 {216.

      [41] USDepartmentofEnergy.Office of ElectricityDeliveryandEnergyReliability.Smart Gridresearchanddevelopment,Multi-YearProgramPlan(mypp);2012.

      [42] ShafiiullaGM,OoAMT,JarvisD,AliABMS,WolfsP.Potential challenges: Integratingrenewableenergywithsmartgrid.2othAustraliasianuniversities Powerengineeringconfernces(AUPEC);2010.P.1-6.

      [43] N.Dahal, R. L. King, V. Madani, Online dimension reductionofsynchrophasor data, in: Proc. IEEE PES Transmission and Distribution Conference and Exposition (T&D), 2012, pp. 1-7.

      [44] L. Xie, Y. Chen, P. R. Kumar, Dimensionality reduction of synchrophasor data for early event Detection: Line arrivedanaylsis,IEEE Trans. Power Syst. 29 (6)(2014)2784-2794.

      [45] J. Taheri, A. Y. Zomaya, Artificial neuralnetworks,in: HandbookofNature-Inspired and Innovative Computing, Springer US, 2006,pp. 147-185.

      [46] M. N. Q. Macedo, J. J. M. Galo, L. A. L. de Almeida, A. C. de C. Lima, Demand side Managementusing artificialneuralnetworks in a smart grid environment, Renewable Sustain.EnergyRev. 41 (0) (2015)128-133.

      [47] S. V. Verdu, M. O. Garcia, F. Franco,N. Encinas,A. G. Marin, A. Molina, E. G. Lazaro Characterization and identification of electrical customers through the use of self-organizing maps and daily loadparameters,in:IEEE Power Systems Conference and Exposition(PES), Vol. 2,2004,pp. 899-906.

      [48] A. Monti, F. Ponci, Power grids of the future: Why smart meanscomplex,in:Proc.IEEE Complexity in Engineering (COMPENG10),2010,pp.7-11.

      [49] A. Sancho-Asensio,J. Navarro, i. Arrietta- Salinas, J. E. Ahmend_ariz_I-nogo, V. Jim_enez- Ruano, A. Zaballos, E. Golobardes, Improving data partition schemes in smart grids via clustering Data streams, Expert Syst. Appl. 41 (13) (2014)5832 -5842.

      [50] <>[lastaccessedon16thApril2014].

      [51] Gupta A SainiRP. SharmaMP.Steady-statemodellingofhybridenergy Systemforoffgridelectrification ofclusterofvillages.RenewEnergy2010; 35(2):520-35.




Article ID: 12540
DOI: 10.14419/ijet.v7i2.26.12540

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.