Related references
Note: Only part of the references are listed.Combining Probabilistic Load Forecasts
Yi Wang et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques
Jiayu Hu et al.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2019)
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression
Mandi Sharifzadeh et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)
Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms
P. Shine et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Alex J. Cannon
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2018)
Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine
Mehdi Rafiei et al.
IEEE TRANSACTIONS ON SMART GRID (2018)
Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting
Hadrien Verbois et al.
SOLAR ENERGY (2018)
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
Chaopeng Shen
WATER RESOURCES RESEARCH (2018)
Inside or Outside: Quantifying Extrapolation Across River Networks
Douglas J. Booker et al.
WATER RESOURCES RESEARCH (2018)
Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction and Downscaling Using Surface Observations
S. Monhart et al.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2018)
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
Frederik Kratzert et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2018)
Different Models for Forecasting Wind Power Generation: Case Study
David Barbosa de Alencar et al.
ENERGIES (2017)
Composite quantile regression neural network with applications
Qifa Xu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Forecasting quantiles of day-ahead electricity load
Z. Li et al.
ENERGY ECONOMICS (2017)
Technical note: Combining quantile forecasts and predictive distributions of streamflows
Konrad Bogner et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2017)
Quantile Regression in Regional Frequency Analysis: A Better Exploitation of the Available Information
D. Ouali et al.
JOURNAL OF HYDROMETEOROLOGY (2016)
Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics
Maxime Taillardat et al.
MONTHLY WEATHER REVIEW (2016)
Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods
Konrad Bogner et al.
WATER (2016)
Multivariate adaptive regression splines and neural network models for prediction of pile drivability
Wengang Zhang et al.
GEOSCIENCE FRONTIERS (2016)
Forecasting Electricity Consumption with Neural Networks and Support Vector Regression
Gamze Ogcu et al.
8TH INTERNATIONAL STRATEGIC MANAGEMENT CONFERENCE (2012)
Estimating conditional quantiles with the help of the pinball loss
Ingo Steinwart et al.
BERNOULLI (2011)
Quantile regression neural networks: Implementation in R and application to precipitation downscaling
Alex J. Cannon
COMPUTERS & GEOSCIENCES (2011)
Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
Tilmann Gneiting et al.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS (2011)
Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system
K. Bogner et al.
WATER RESOURCES RESEARCH (2011)
Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents
Issa F. Zakeri et al.
JOURNAL OF APPLIED PHYSIOLOGY (2010)
Comparison of universal approximators incorporating partial monotonicity by structure
Alexey Minin et al.
NEURAL NETWORKS (2010)
Electric load forecasting by support vector model
Wei-Chiang Hong
APPLIED MATHEMATICAL MODELLING (2009)
An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools
D. Viviroli et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2009)
Statistical downscaling of extreme precipitation events using censored quantile regression
P. Friederichs et al.
MONTHLY WEATHER REVIEW (2007)
Verification tools for probabilistic forecasts of continuous hydrological variables
F. Laio et al.
HYDROLOGY AND EARTH SYSTEM SCIENCES (2007)
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2007)
Models for mid-term electricity demand forecasting incorporating weather influences
S Mirasgedis et al.
ENERGY (2006)
Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation
T Gneiting et al.
MONTHLY WEATHER REVIEW (2005)
Regularization and variable selection via the elastic net
H Zou et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2005)
A tutorial on support vector regression
AJ Smola et al.
STATISTICS AND COMPUTING (2004)
Using weather ensemble predictions in electricity demand forecasting
JW Taylor et al.
INTERNATIONAL JOURNAL OF FORECASTING (2003)
Stochastic gradient boosting
JH Friedman
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
Share Paper
