Power load probability density forecasting using Gaussian process quantile regression
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Yandong Yang | Shufang Li | Wenqi Li | Meijun Qu | Wenqi Li | Yandong Yang | Meijun Qu | Shufang Li
[1] Ming-Wei Chang,et al. Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.
[2] Dan Cornford,et al. Gaussian Process Quantile Regression using Expectation Propagation , 2012, ICML.
[3] Tao Hong,et al. Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts , 2017, IEEE Transactions on Smart Grid.
[4] Raymond Chiong,et al. Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection , 2015 .
[5] Amir F. Atiya,et al. Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.
[6] Saeid Nahavandi,et al. Prediction Interval Construction and Optimization for Adaptive Neurofuzzy Inference Systems , 2011, IEEE Transactions on Fuzzy Systems.
[7] Birgitte Bak-Jensen,et al. ARIMA-Based Time Series Model of Stochastic Wind Power Generation , 2010, IEEE Transactions on Power Systems.
[8] Roger Woodard,et al. Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.
[9] Shanlin Yang,et al. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function , 2016 .
[10] Tom Minka,et al. Expectation Propagation for approximate Bayesian inference , 2001, UAI.
[11] Michael W. Mahoney,et al. Quantile Regression for Large-Scale Applications , 2013, SIAM J. Sci. Comput..
[12] Abbas Khosravi,et al. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[13] H Zareipour,et al. Wind Power Prediction by a New Forecast Engine Composed of Modified Hybrid Neural Network and Enhanced Particle Swarm Optimization , 2011, IEEE Transactions on Sustainable Energy.
[14] Göksel Biricik,et al. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market , 2017, PloS one.
[15] T. Chiang,et al. Stock Returns and Risk: Evidence from Quantile , 2012 .
[16] Rob J. Hyndman,et al. Bandwidth selection for kernel conditional density estimation , 2001 .
[17] Alexander J. Smola,et al. Nonparametric Quantile Estimation , 2006, J. Mach. Learn. Res..
[18] Rob J. Hyndman,et al. Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression , 2016, IEEE Transactions on Smart Grid.
[19] Jian Xiao,et al. Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression , 2018, IEEE Transactions on Industrial Electronics.
[20] Paras Mandal,et al. A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.
[21] Jean-Baptiste Fiot,et al. Electricity Demand Forecasting by Multi-Task Learning , 2015, IEEE Transactions on Smart Grid.
[22] Keming Yu,et al. Bayesian quantile regression , 2001 .
[23] Guillaume Foggia,et al. Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems , 2016, IEEE Transactions on Power Systems.
[24] Chia-Nan Ko,et al. Short-term load forecasting using lifting scheme and ARIMA models , 2011, Expert Syst. Appl..
[25] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[26] Oscar Castillo,et al. A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..
[27] James Robert Lloyd,et al. GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes , 2014 .
[28] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[29] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[30] H. Madsen,et al. Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression , 2014 .
[31] Saeid Nahavandi,et al. Construction of Optimal Prediction Intervals for Load Forecasting Problems , 2010, IEEE Transactions on Power Systems.
[32] Rob J Hyndman,et al. Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.
[33] D. Srinivasan,et al. Interval Type-2 Fuzzy Logic Systems for Load Forecasting: A Comparative Study , 2012, IEEE Transactions on Power Systems.
[34] E. Caamaño-Martín,et al. Improving photovoltaics grid integration through short time forecasting and self-consumption , 2014 .
[35] Joshua B. Tenenbaum,et al. Structure Discovery in Nonparametric Regression through Compositional Kernel Search , 2013, ICML.
[36] M. Negnevitsky,et al. Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.
[37] R. Weron,et al. Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging , 2016 .
[38] D. W. Scott,et al. Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .
[39] Aki Vehtari,et al. GPstuff: Bayesian modeling with Gaussian processes , 2013, J. Mach. Learn. Res..
[40] Q. Henry Wu,et al. Electric Load Forecasting Based on Locally Weighted Support Vector Regression , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[41] Shuo Wang,et al. Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory , 2017 .