论文信息 - ICT Innovations 2019. Big Data Processing and Mining: 11th International Conference, ICT Innovations 2019, Ohrid, North Macedonia, October 17–19, 2019, Proceedings - 字舞流文

ICT Innovations 2019. Big Data Processing and Mining: 11th International Conference, ICT Innovations 2019, Ohrid, North Macedonia, October 17–19, 2019, Proceedings

of Keynotes Machine Learning Optimization and Modeling: Challenges and Solutions to Data Deluge

Krishna M. Sivalingam | Alfredo Cuzzocrea | Simone Diniz Junqueira Barbosa | Xiaokang Yang | Junsong Yuan | Dominik Ślęzak | Phoebe Chen | Orhun Kara | Takashi Washio | Ting Liu | Gjorgji Madjarov | Xiaoyong Du | Sonja Gievska | S. Gievska | Junsong Yuan | T. Washio | D. Ślęzak | Simone Diniz Junqueira Barbosa | Phoebe Chen | A. Cuzzocrea | Xiaoyong Du | Orhun Kara | Ting Liu | K. Sivalingam | Xiaokang Yang | Gjorgji Madjarov

[1] S. J. Kiartzis,et al. A neural network short term load forecasting model for the Greek power system , 1996 .

[2] Chuntian Cheng,et al. Short-Term Load Forecasting Using Support Vector Machine with SCE-UA Algorithm , 2007, Third International Conference on Natural Computation (ICNC 2007).

[3] Norizan Mohamed,et al. Electricity load demand forecasting using exponential smoothing methods , 2013 .

[4] M. Crawford,et al. An Adaptive Nonlinear Predictor with Orthogonal Escalator Structure for Short-Term Load Forecasting , 1989, IEEE Power Engineering Review.

[5] Rainer Göb,et al. Electrical load forecasting by exponential smoothing with covariates , 2013 .

[6] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[7] J. Friedman. Stochastic gradient boosting , 2002 .

[8] Lambros Ekonomou,et al. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .

[9] Lee-Ing Tong,et al. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[10] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .

[11] Shyh-Jier Huang,et al. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[12] Ramesh R. Rao,et al. Short-term Electric Load Prediction Using Multiple Linear Regression Method , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[13] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .

[14] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.

[15] S. Sathiya Keerthi,et al. Improvements to the SMO algorithm for SVM regression , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16] Patrick P. K. Chan,et al. Random forest based ensemble system for short term load forecasting , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[17] Daniel L. Marino,et al. Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[18] Ran Li,et al. Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.

[19] Grzegorz Dudek,et al. Short-Term Load Forecasting Using Random Forests , 2014, IEEE Conf. on Intelligent Systems.

[20] T. Senjyu,et al. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market , 2006 .

[21] S. Surender Reddy,et al. Short term electrical load forecasting using back propagation neural networks , 2014, 2014 North American Power Symposium (NAPS).