Power load probability density forecasting using Gaussian process quantile regression
- 1 March 2018
- journal article
- research article
- Published by Elsevier BV in Applied Energy
- Vol. 213, 499-509
- https://doi.org/10.1016/j.apenergy.2017.11.035
Abstract
No abstract availableFunding Information
- Natural Science Foundation of China (61672292, 61300162)
- State Grid Corporation 2016 science and technology project
This publication has 30 references indexed in Scilit:
- Improving photovoltaics grid integration through short time forecasting and self-consumptionApplied Energy, 2014
- GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processesInternational Journal of Forecasting, 2014
- Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction IntervalsIEEE Transactions on Neural Networks and Learning Systems, 2013
- Stock Returns and Risk: Evidence from QuantileJournal of Risk and Financial Management, 2012
- A new approach for time series prediction using ensembles of ANFIS modelsExpert Systems with Applications, 2011
- Very Short-Term Wind Forecasting for Tasmanian Power GenerationIEEE Transactions on Power Systems, 2006
- Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001IEEE Transactions on Power Systems, 2004
- Bayesian quantile regressionStatistics & Probability Letters, 2001
- Bandwidth selection for kernel conditional density estimationComputational Statistics & Data Analysis, 2001
- Regression QuantilesEconometrica, 1978