• [1]
    Y. Bengio, P. Simard, and P. Frasconi, “Learning Long-Term Dependencies with Gradient Descent Is Difficult,” IEEE Trans. Neural Networks, 5:2 (1994), 157166.
  • [2]
    A. Bhargava, “On the Theory of Testing for Unit Roots in Observed Time Series,” Rev. Econom. Stud., 53:3 (1986), 369384.
  • [3]
    C. E. Borges, Y. K. Penya, and I. Fernández, “Optimal Combined Short-Term Building Load Forecasting,” Proc. IEEE PES Innovative Smart Grid Technol. Asia Conf. (ISGT '11) (Perth, Aus., 2011).
  • [4]
    J. Cao, A. Chen, T. Bu, and A. Buvaneswari, “Monitoring Time-Varying Network Streams Using State-Space Models,” Proc. 28th IEEE Internat. Conf. on Comput. Commun. (INFOCOM '09) (Rio de Janeiro, Bra., 2009), pp. 27212725.
  • [5]
    O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing Multiple Parameters for Support Vector Machines,” Mach. Learn., 46:1-3 (2002), 131159.
  • [6]
    W. Charytoniuk, M. S. Chen, and P. Van Olinda, “Nonparametric Regression Based Short-Term Load Forecasting,” IEEE Trans. Power Syst., 13:3 (1998), 725730.
  • [7]
    B.-J. Chen, M.-W. Chang, and C.-J. Lin, “Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001,” IEEE Trans. Power Syst., 19:4 (2004), 18211830.
  • [8]
    W. S. Cleveland and S. J. Devlin, “Locally-Weighted Regression: An Approach to Regression Analysis by Local Fitting,” J. Amer. Statist. Assoc., 83:403 (1988), 596610.
  • [9]
    C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., 20:3 (1995), 273297.
  • [10]
    V. Dordonnat, S. J. Koopman, M. Ooms, A. Dessertaine, and J. Collet, “An Hourly Periodic State Space Model for Modelling French National Electricity Load,” Internat. J. Forecasting, 24:4 (2008), 566587.
  • [11]
  • [12]
    S. Fan and L. Chen, “Short-Term Load Forecasting Based on an Adaptive Hybrid Method,” IEEE Trans. Power Syst., 21:1 (2006), 392401.
  • [13]
    E. A. Feinberg and D. Genethliou, “Load Forecasting,” Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence (J. H. Chow, F. F. Wu, and J. A. Momoh, eds.), Springer, New York, 2005, pp. 269285.
  • [14]
    P. Goovaerts, Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, 1997.
  • [15]
    G. Gross and F. D. Galiana, “Short-Term Load Forecasting,” Proc. IEEE, 75:12 (1987), 15581573.
  • [16]
    F. Gullo, G. Ponti, A. Tagarelli, S. Iiritano, M. Ruffolo, and D. Labate, “Low-Voltage Electricity Customer Profiling Based on Load Data Clustering,” Proc. 13th Internat. Database Eng. and Applications Symp. (IDEAS '09) (Cetraro, Calabria, Ita., 2009), pp. 330333.
  • [17]
    H. Hahn, S. Meyer-Nieberg, and S. Pickl, “Electric Load Forecasting Methods: Tools for Decision Making,” European J. Oper. Res., 199:3 (2009), 902907.
  • [18]
    T. M. Hansen, “mGstat: A Geostatistical Matlab Toolbox,” <>.
  • [19]
    A. Harvey and S. J. Koopman, “Forecasting Hourly Electricity Demand Using Time-Varying Splines,” J. Amer. Statist. Assoc., 88:424 (1993), 12281236.
  • [20]
    J. D. Hobby, A. Shoshitaishvili, and G. H. Tucci, “Analysis and Methodology to Segregate Residential Electricity Consumption in Different Taxonomies,” IEEE Trans. Smart Grid, 3:1 (2012), 217224.
  • [21]
    T. Hong, P. Wang, and H. L. Willis, “A Naïve Multiple Linear Regression Benchmark for Short Term Load Forecasting,” Proc. IEEE Power and Energy Soc. Gen. Meeting (Detroit, MI, 2011).
  • [22]
    A. Khotanzad, R. Afkhami-Rohani, and D. Maratukulam, “ANNSTLF—Artificial Neural Network Short-Term Load Forecaster—Generation Three,” IEEE Trans. Power Syst., 13:4 (1998), 14131422.
  • [23]
    J. Z. Kolter and M. J. Johnson, “REDD: A Public Data Set for Energy Disaggregation Research,” Proc. KDD Workshop on Data Mining Applications in Sustainability (SustKDD '11) (San Diego, CA, 2011).
  • [24]
    D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root,” J. Econometrics, 54:1-3 (1992), 159178.
  • [25]
    E. Kyriakides and M. Polycarpou, “Short Term Electric Load Forecasting: A Tutorial,” Trends in Neural Computation (K. Chen and L. Wang, eds.), Springer, Berlin, New York, 2007, pp. 391418.
  • [26]
    D. Mattera and S. Haykin, “Support Vector Machines for Dynamic Reconstruction of a Chaotic System,” Advances in Kernel Methods: Support Vector Learning (B. Schölkopf, C. J. C. Burges, and A. J. Smola, eds.), MIT Press, Cambridge, MA, 1999, pp. 211242.
  • [27]
    A. Muñoz, E. F. Sánchez-Úbeda, A. Cruz, and J. Marín, “Short-Term Forecasting in Power Systems: A Guided Tour,” Handbook of Power Systems II: Energy Systems (S. Rebennack, P. M. Pardalos, M. V. F. Pereira, and N. A. Iliadis, eds.), Springer, Heidelberg, New York, 2010, pp. 129160.
  • [28]
    E. A. Nadaraya, “On Estimating Regression,” Theory Probab. Appl., 9:1 (1964), 141142.
  • [29]
    T. N. Palmer, G. J. Shutts, R. Hagedorn, F. J. Doblas-Reyes, T. Jung, and M. Leutbecher, “Representing Model Uncertainty in Weather and Climate Prediction,” Annual Review of Earth and Planetary Sciences, Volume 33 (R. Jeanloz, A. L. Albee, and K. C. Burke, eds.), Annual Reviews, Palo Alto, CA, May 2005, pp. 163193.
  • [30]
    A. Pigazo and V. M. Moreno, “Estimation of Electrical Power Quantities by Means of Kalman Filtering,” Kalman Filter: Recent Advances and Applications (A. Pigazo and V. M. Moreno, eds.), In-Tech, Rijeka, Cro., Apr. 2009, pp. 375396.
  • [31]
    S. Rogai, “Keynote I. Telegestore Project Progresses and Results,” IEEE Internat. Symp. on Power Line Commun. and Its Applications (ISPLC '07) (Pisa, Ita., 2007).
  • [32]
    J.-H. Shin, B.-J. Yi, Y.-I. Kim, H.-G. Lee, and K. H. Ryu, “Spatiotemporal Load-Analysis Model for Electric Power Distribution Facilities Using Consumer Meter-Reading Data,” IEEE Trans. Power Delivery, 26:2 (2011), 736743.
  • [33]
    R. P. Singh, P. X. Gao, and D. J. Lizotte, “On Hourly Home Peak Load Prediction,” Proc. 3rd IEEE Internat. Conf. on Smart Grid Commun. (SmartGridComm '12) (Tainan, Twn., 2012), pp. 163168.
  • [34]
    A. J. Smola and B. Schölkopf, “A Tutorial on Support Vector Regression,” Stat. Comput., 14:3 (2004), 199222.
  • [35]
    L. J. Soares and M. C. Medeiros, “Modeling and Forecasting Short-Term Electricity Load: A Comparison of Methods with an Application to Brazilian Data,” Internat. J. Forecasting, 24:4 (2008), 630644.
  • [36]
    R. G. Steadman, “A Universal Scale of Apparent Temperature,” J. Climate Applied Meteorology, 23:12 (1984), 16741687.
  • [37]
    J. W. Taylor and P. E. McSharry, “Short-Term Load Forecasting Methods: An Evaluation Based on European Data,” IEEE Trans. Power Syst., 22:4 (2007), 22132219.