Towards Dynamic Demand Response On Efficient Consumer Grouping Algorithmics
暂无分享,去创建一个
Viktor K. Prasanna | Charalampos Chelmis | Marc Frîncu | Ranjan Pal | V. Prasanna | C. Chelmis | M. Frîncu | R. Pal
[1] Yasuhiro Hayashi,et al. A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households , 2013, IEEE Transactions on Smart Grid.
[2] Sarvapali D. Ramchurn,et al. Putting the 'smarts' into the smart grid , 2012, Commun. ACM.
[3] M. Inaba. Application of weighted Voronoi diagrams and randomization to variance-based k-clustering , 1994, SoCG 1994.
[4] Dimitrios Gunopulos,et al. Iterative Incremental Clustering of Time Series , 2004, EDBT.
[5] Dorit S. Hochbaum,et al. Various notions of approximations: good, better, best, and more , 1996 .
[6] Francisco Martinez Alvarez,et al. Energy Time Series Forecasting Based on Pattern Sequence Similarity , 2011, IEEE Transactions on Knowledge and Data Engineering.
[7] Vijay Arya,et al. Individual and Aggregate Electrical Load Forecasting: One for All and All for One , 2015, e-Energy.
[8] Anna Choromanska,et al. Online Clustering with Experts , 2012, AISTATS.
[9] Angie King. Online k-Means Clustering of Nonstationary Data , 2012 .
[10] Tomás Feder,et al. Optimal algorithms for approximate clustering , 1988, STOC '88.
[11] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[12] David S. Johnson,et al. Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .
[13] Sariel Har-Peled,et al. On coresets for k-means and k-median clustering , 2004, STOC '04.
[14] Qiang Fu,et al. YADING: Fast Clustering of Large-Scale Time Series Data , 2015, Proc. VLDB Endow..
[15] Viktor K. Prasanna,et al. Challenge: On Online Time Series Clustering for Demand Response: Optic - A Theory to Break the 'Curse of Dimensionality' , 2015, e-Energy.
[16] Yik-Chung Wu,et al. Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges , 2012, IEEE Signal Processing Magazine.
[17] Teofilo F. GONZALEZ,et al. Clustering to Minimize the Maximum Intercluster Distance , 1985, Theor. Comput. Sci..
[18] Marek Karpinski,et al. Approximation schemes for clustering problems , 2003, STOC '03.
[19] Feller William,et al. An Introduction To Probability Theory And Its Applications , 1950 .
[20] Rajeev Motwani,et al. Incremental clustering and dynamic information retrieval , 1997, STOC '97.
[21] Eamonn J. Keogh,et al. An indexing scheme for fast similarity search in large time series databases , 1999, Proceedings. Eleventh International Conference on Scientific and Statistical Database Management.
[22] S. L. HAKIMIt. AN ALGORITHMIC APPROACH TO NETWORK LOCATION PROBLEMS. , 1979 .
[23] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[24] Yingying Li,et al. Research on incremental clustering , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).
[25] D. Eppstein,et al. Approximation algorithms for geometric problems , 1996 .
[26] G. Chicco,et al. Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.
[27] J. Matou. On Approximate Geometric K-clustering , 1999 .
[28] Xinghuo Yu,et al. The New Frontier of Smart Grids , 2011, IEEE Industrial Electronics Magazine.
[29] Hesham K. Alfares,et al. Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..
[30] David M. Mount,et al. A local search approximation algorithm for k-means clustering , 2002, SCG '02.
[31] Eamonn J. Keogh,et al. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification , 2010, Data Mining and Knowledge Discovery.
[32] Nir Ailon,et al. Streaming k-means approximation , 2009, NIPS.
[33] Eamonn J. Keogh,et al. Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets , 2013, SDM.
[34] Hans W. Guesgen,et al. Unsupervised Learning of Human Behaviours , 2011, AAAI.
[35] C. Senabre,et al. Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps , 2006, IEEE Transactions on Power Systems.
[36] Alan M. Frieze,et al. Clustering Large Graphs via the Singular Value Decomposition , 2004, Machine Learning.
[37] N.D. Hatziargyriou,et al. Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers , 2007, IEEE Transactions on Power Systems.
[38] Sudipto Guha,et al. Clustering Data Streams: Theory and Practice , 2003, IEEE Trans. Knowl. Data Eng..
[39] T. Warren Liao,et al. Clustering of time series data - a survey , 2005, Pattern Recognit..
[40] Ram Rajagopal,et al. Utility customer segmentation based on smart meter data: Empirical study , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[41] Ram Rajagopal,et al. Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.
[42] Satish Rao,et al. Learning Mixtures of Product Distributions Using Correlations and Independence , 2008, COLT.
[43] Michael McGill,et al. Introduction to Modern Information Retrieval , 1983 .
[44] Eamonn J. Keogh,et al. Exact Discovery of Time Series Motifs , 2009, SDM.
[45] Viktor K. Prasanna,et al. Accurate and efficient selection of the best consumption prediction method in smart grids , 2014, 2014 IEEE International Conference on Big Data (Big Data).
[46] Pavel Berkhin,et al. A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.
[47] P. Postolache,et al. Load pattern-based classification of electricity customers , 2004, IEEE Transactions on Power Systems.
[48] Olatz Arbelaitz,et al. An extensive comparative study of cluster validity indices , 2013, Pattern Recognit..
[49] Eamonn J. Keogh,et al. Logical-shapelets: an expressive primitive for time series classification , 2011, KDD.
[50] Steven J. Moss,et al. Market Segmentation and Energy Efficiency Program Design , 2008 .
[51] Mary Inaba,et al. Applications of weighted Voronoi diagrams and randomization to variance-based k-clustering: (extended abstract) , 1994, SCG '94.
[52] Juan Shishido,et al. Smart Meter Data Quality Insights , 2012 .
[53] Viktor K. Prasanna,et al. Extracting discriminative shapelets from heterogeneous sensor data , 2014, 2014 IEEE International Conference on Big Data (Big Data).
[54] Johanna L. Mathieu,et al. Variability in automated responses of commercial buildings and industrial facilities to dynamic elec , 2011 .
[55] Shai Ben-David,et al. Relating Clustering Stability to Properties of Cluster Boundaries , 2008, COLT.
[56] Peter Willett,et al. Recent trends in hierarchic document clustering: A critical review , 1988, Inf. Process. Manag..
[57] C. Greg Plaxton,et al. Optimal Time Bounds for Approximate Clustering , 2002, Machine Learning.
[58] Yogesh L. Simmhan,et al. Scalable prediction of energy consumption using incremental time series clustering , 2013, 2013 IEEE International Conference on Big Data.