Smart grids, or intelligent electricity grids that utilize modern IT/communication/control technologies, become a global trend nowadays. Smart Grids which enable two-way communication and monitoring between service providers and end-users need novel computational intelligent algorithms for supporting generation of power from wide range of sources, efficient energy distribution, and sustainable consumption. Sustainability is of great importance due to increasing demands and limited resources. Many problem classes in sustainable energy systems are data mining, optimization, and control tasks. The aim of this paper is to focus on the existing electricity generation infrastructure, electricity consumption behavior of the consumers and the need for Smart Grid. The various methods that have been concentrated on are that of machine learning and data mining techniques that can be mapped to these smart grid environments. We use publicly available smart grid datasets such as: Residential Electricity consumption survey (RECS) dataset conducted in US; US SMART Home Microgrid dataset; Reference Energy Disaggregation dataset (REDD) and Almanac of Minutely Power (AMPds) aggregation Dataset in our analysis in order to optimize the energy consumption for sustainability. We utilize Gaussian process regression with Radial basis function (RBF) kernel, Best First Tree (BFTree) and Ordered weighted average fuzzy-rough K-nearest neighbor (OWAKNN) with equal width(EWD) and Genetic algorithm based Discretization (GAD) in our approach to predict and forecast the consumer behavior in electricity consumption. The result obtained in terms of errors will be an ingredient to make effective decisions for developing a sustainable smart grid infrastructure.
KavousianA., RajagopalR. and FischerM., A method to analyze large data sets of residential electricity consumption to inform data-driven energy efficiency, CIFE Working Paper #WP130, (June 2012), STANFORD UNIVERSITY. http://cife.stanford.edu/sites/default/files/WP130_0.pdf.
2.
SalehA.O.M. and LaughtonM.A., Cluster analysis of power-system networks for array processing solutions, IEE Proceedings132(4) (1985), 172-178.
3.
WillmottC.J. and MatsuuraK., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Climate Research30(1) (2005), 79-82.
4.
LiuaC., RatheraZ.H., ChenaZ. and BakC.L., An overview of decision tree applied to power systems, International Journal of Smart Grid and Clean Energy2(3) (October 2013), 413-419.
5.
CornelisC., VerbiestN. and JensenR., Ordered weighted average based fuzzy rough sets, Proceedings of the 5th International Conference on Rough Set and Knowledge Technology (2010), 78-85.
6.
LiD.H.W., CheungG.H.W. and LamJ.C., Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong, Energy Conversion and Management46 (2005), 1107-1118.
7.
MackayD.J.C., Introduction to Gaussian Processes, Dept of Physics, Cambridge University, UK, 1998, http://www.inference.phy.cam.ac.uk/mackay/gpB.pdf.
8.
BrownE.R., Impact of Smart Grid on distribution system design, Industry Applications Conference, IEEE Press (2008), 1-4.
SalfnerF., LenkM. and HumboldtM.M., A survey of online failure prediction methods, (Jan 2009), http://informatik.huberlin.de/Members/salfner/publications/salfner10survey.pdf.
11.
BhatiaG. and SaneS., A survey of data mining techniques for smart grid systems, International Journal of Electronics Communication and Computer Engineering3(6) (2012), 1325-1329.
12.
FarhangiH., The path of the smart grid, IEEE Power and Energy Magazine8(1) (2010), 18-28.
13.
ZhangH.T., XuF.Y. and ZhouL., Artificial neural network for load forecasting in smart grid, in: Proceedings of the 2010 International Conference on Machine Learning and Cybernetics (ICMLC)6 (2010), 3200-3205.
14.
MoriH., State-of-the-art overview on data mining in power systems, in: Proceedings of the 2006 IEEE PES Power Systems Conference and Exposition (PSCE), (2006), 33-34.
15.
ShiH., Best-first decision tree learning, Hamilton, NZ, 2007, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2862.
16.
FernandezI., BorgesC.E. and PenyaY.K., Efficient building load forecasting, in: Proceedings of the 16th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), (2011), 1-8.
17.
WittenI.H. and FrankE., Data mining-practical machine learning tools and techniques, 2nd Editions, Elsevier, 2005.
18.
SteelJ.A., McDonaldJ.R. and D'ArcyC., Knowledge discovery in databases: Applications in the electrical power engineering domain, in: Proceedings of IEE Colloquium IT Strategies Information Overload, (Dec 1997), 8/1-8/4.
19.
DengJ. and JirutitijaroenP., Short-term load forecasting using time series analysis: A case study for singapore, in: Proceedings of the 2010 IEEE Conference on Cybernetics and Intelligent Systems (CIS), (2010), 231-236.
20.
FroehlichJ., LarsonE., GuptaS., CohnG., ReynoldsM.S. and PatelS.N., Disaggregated end-use energy sensing for the smart grid IEEE pervasive computing, Special Issue on Smart Energy Systems10(1) (Jan-Mar 2011) 28-39.
21.
MoraisJ., PiresY., CardosoC. and KlautauA., An overview of data mining techniques applied to power systems, in: Data Mining and Knowledge Discovery in Real Life Applications, PonceJ. and KarahocaA., eds, I-Tech Education and Publishing, 2009, pp. 419-438. http://cdn.intechopen.com/pdfs-wm/5962.pdf.
22.
LiJ.Q., WangS.L., NiuC.L. and LiuJ.Z., Research and application of data mining technique in power plant, in: Proceedings of the 2008 International Symposium on Computational Intelligence and Design (ISCID)2 (2008), 250-253.
23.
TaylorJ.W., An evaluation of methods for very short term electricity demand forecasting using minute-by-minute British data, International Journal of Forecasting24 (2008), 645-658.
24.
KolterJ.Z. and JohnsonM.J., REDD: A public data set for energy disaggregation research, in: proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, ACM Press, 2011, http://redd.csail.mit.edu/kolter-kddsust11.pdf.
25.
FriedmanJ., HastieT. and TibshiraniR., Additive logistic regression: A statistical view of boosting, Annals of statistics28(2) (2000), 337-407.
26.
RogersK.M., Data-enhanced applications for power systems analysis, Ph.D. dissertation, University of Illinois at Urbana-Champaign, 2011, https://www.ideals.illinois.edu/bitstream/handle/…/Rogers_Katherine.pdf.
27.
WuL., KaiserG., RudinC. and AndersonR., Data quality assurance and performance measurement of data mining for preventive maintenance of power grid, in: Proc of KDD4Service '11, San Diego, California, USA, ACM Press, (2011), 28-32. http://web.mit.edu/rudin/www/docs/WuEtAl11KDDService.pdf.
28.
McGranaghanM., Making connections: Asset management and the smart grid, IEEE Power and Energy Magazine8 (2010), 16-22.
29.
HossainR., OoA.M.T. and AliA.B.M.S., Hybrid prediction method for solar power using different computational intelligence algorithms, Smart Grid and Renewable Energy4 (2013), 76-87.
30.
UslarM., RohjansandS., BleikerR., GonzálezJ., SpechtM., SudingT. and WeideltT., Survey of smart grid standardization studies and recommendations - part 2, IEEE PES'10, (2010), 1-6.
31.
NIST Framework and Road map for Smart Grid Interoperability Standards, Release 2.0. Offices of the National Coordinator for Smart Grid Interoperability, National Institute of Standards and Technology, (February 2012). http://www. nist.gov/smartgrid/upload/NIST_Framework_Release_2-0_corr.pdf.
32.
KramerO., Computational Intelligence and sustainable energy: Case studies and applications, TR-10-010, 1-12 (November 2010).
33.
KramerO., GiesekeF. and SatzgerB., Wind energy prediction and monitoring with neural computation, Neurocomputing109 (2013), 84-93.
GrossP., BoulangerA., AriasM., WaltzD., LongP.M., LawsonC., AndersonR., KoenigM., MastrocinqueM., FairechioW., JohnsonJ.A., LeeS., DohertyF. and KressnerA., Predicting electricity distribution feeder failures using machine learning susceptibility analysis, in: Proceedings of the 18th Conference on Innovative Applications of Artificial Intelligence (IAAI)2 (2006), 1705-1711.
36.
MyrdaP., Optimizing assets: Smart grid-enabled asset management, IEEE Power and Energy Magazine8 (2010), 109-112.
37.
AmbikairajahR., PhungB.T., RavishankarJ., BlackburnT.R. and LiuZ., Smart sensors and online condition monitoring of high voltage cables for the smart grid, in: Proceedings of the 14th International Middle East Power Systems Conference (MEPCON), Egypt, December 19-21, (2010), 807-811.
38.
EdwardsR.E., NewJ. and ParkerL.E., Predicting future hourly residential electrical consumption: A machine learning case study, Energy and Buildings49 (2012), 591-603.
39.
HassanR. and RadmanG., Survey on smart grid, IEEE SoutheastCon 2010 (2010), 210-213.
40.
MallikR., SardaN., KarguptaH. and BandyopadhyayS., Distributed Data Mining for Sustainable Smart Grids, SustKDD (2011), (August 2011), San Diego, CA, USA, ACM Press. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.455.5145&rep=rep1&type=pdf.
41.
AndersonR.N., Building the energy Internet, Economist March11 (2004).
42.
RamchurnS.D., VytelingumP., RogersA. and JenningsN.R., Putting the smarts into the smart grid: A grand challenge for artificial intelligence, Communications of the ACM55 (2012), 86-97.
43.
YagerR.R., On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man, and Cybernetics18 (1988), 183-190.
44.
BarkerS., MishraA., IrwinD., CecchetE., ShenoyP. and AlbrechtJ., Smart: An open data set and tools for enabling research in sustainable homes, Proceedings of the 2012 Workshop on Data Mining Applications in Sustainability (SustKDD 2012), Beijing, China, (August 2012). http://lass.cs.umass.edu/papers/pdf/sustkdd12-smart.pdf.
45.
ChuaS.-L. and MarslandS. and GuesgenH.W., Behaviour recognition in smart homes, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, AAAI press 3 (2011), 2788-2789.
46.
RamchurnS.D., VytelingumP., RogersA. and JenningsN.R., Putting the `smarts' into the smart grid: A grand challenge for artificial intelligence, Communications of the ACM55(4) (April 2012), 86-97.
47.
Smart Grid Sensing, Monitoring and Control Systems: Market Opportunities 2011 Nano-239, (March 2011). http://www.re-searchandmarkets.com/research/be966a/smart_grid_sensing.
48.
KrishnaswamyS., Energy analytics: When data mining meets the smart grid, 2012. http://smartgrid.i2r.a-star.edu.sg/2012/slides/i2r.pdf.
49.
KherS., NuttV. and DasguptaD., A prediction model for anomalies in smart grid with sensor network, in: Proceedings of the Eighth Annual Cyber Security and Information Intelligence (Article-160), (2013).
50.
UlloS.L., VaccaroA. and VelottoG., Performance analysis of ieee 802.15.4 based sensor networks for smart grids communications, Journal of Electrical Engineering: Theory and Application1(3) (2010), 129-134.
51.
MakoninS., PopowichF., BartramL., GillB. and BajicI.V., AMPds: A public dataset for load disaggregation and eco-feedback research, in: Electrical Power and Energy Conference (EPEC), The Annual, (2013), 1-6.
52.
AminS.M. and WollenbergB.F., Toward a SmartGrid: Power delivery for the 21st century, IEEE Power and Energy Magazine3(5) (2005), 34-41.
53.
MadanS., SonW.-K. and BollingerK.E., Applications of data mining for power systems, in: Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)2 (1997), 403-406.
54.
RohjansandS., UslarM., BleikerR., GonzálezJ., SpechtM., SudingT. and WeideltT., Survey of smart grid standardization studies and recommendations, IEEE SmartGridComm '10 (2010), 583-587.
55.
HongT., Short term electric load forecasting, Ph.D. Thesis North Carolina State University, USA, 2010.
56.
SulaymanU. and AlouniT., Smart grid monitoring using local area sensor network, Real-time data acquisition, analysis and management, Proceedings of the IEEE Southeastern 2011 Conference (2011), 444-449.
57.
FayyadU., ShapiroG.P. and SmythP., From data mining to knowledge discovery in databases, AI Magazine, (1996), 37-53.
58.
FigueiredoV., RodriguesF., ValeZ. and GouveiaJ.B., An electric energy consumer characterization framework based on data mining techniques, IEEE Trans Power Syst20(2) (2005), 596-602.
59.
BalijepalliV.S.K.M., KhapardeS.A., GuptaR.P. and PradeepY., Smartgrid initiatives and power market in India, IEEE (2010), 1-7.
60.
Xi Fang, MishraS., XueG. and YangD., Smart grid - The new and improved power grid: A survey, IEEE Communications Surveys & Tutorials, IEEE Press 14(4) (2011), 944-980.
61.
DongaX., JingbX., QiancK., ZhouaW.J.C. and PengX., A smart grid needs smart monitoring, International Journal of Smart Grid and Clean Energy2(3) (2013), 322-328.
62.
LiY., WangJ. and LiS., The fuzzy neural network model of smart grid risk evaluation based on Bayes, Journal of Computers6(1) (2011), 90-97.
63.
ChangY.-W. and LinC.-J., Feature ranking using linear SVM, JMLR: Workshop and Conference Proceedings: WCCI 2008 Workshop on Causality3 (2008), 53-64. http://jmlr.csail.mit.edu/proceedings/papers/v3/chang08a/chang08a.pdf.
64.
AungZ., ToukhyM., WilliamsJ., SanchezA. and HerreroS., Towards accurate electricity load forecasting in smart grids, in: Proceedings of the 4th International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA), (2012), 51-57.
