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Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

Henrik Aalborg Nielsen, Henrik Madsen, Torben Skov Nielsen
2006 Wind Energy  
An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error.  ...  For operational planning it is important to provide information about the situation-dependent uncertainty of a wind power forecast.  ...  The financial support is hereby greatly acknowledged. Furthermore, the authors wish to thank Elsam Kraft A/S and the Danish Meteorological Institute for supplying the data used in this study.  ... 
doi:10.1002/we.180 fatcat:bts36decpjevvakolho23ivy4m

Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information

Julija Tastu, Pierre Pinson, Pierre-Julien Trombe, Henrik Madsen
2014 IEEE Transactions on Smart Grid  
The best performing approach, based on adaptive quantile regression, using spatially corrected point forecasts as input, consistently outperforms the state-of-theart benchmark based on local information  ...  Forecasts of wind power generation in their probabilistic form are a necessary input to decision-making problems for reliable and economic power systems operations in a smart grid context.  ...  ACKNOWLEDGEMENT The authors gratefully acknowledge DONG Energy for the data and financial support provided through the project "Spatio-temporal correction of wind power forecasts".  ... 
doi:10.1109/tsg.2013.2277585 fatcat:bk7yowyagvebzprzfamhougb5a

Probabilistic access forecasting for improved offshore operations

Ciaran Gilbert, Jethro Browell, David McMillan
2020 International Journal of Forecasting  
Methods of generating density forecasts of significant wave height and peak wave period are developed and evaluated.  ...  Scenario forecasts of sea-state variables are generated and used as inputs to a data-driven vessel motion model, based on telemetry recorded during 700 crew transfers.  ...  Acknowledgments This work is supported by the EPSRC Supergen Wind Hub project ORACLES, EP/L014106/1.  ... 
doi:10.1016/j.ijforecast.2020.03.007 fatcat:gpvvl75ls5a7fmqk3lln7bul5i

Statistical metrics for assessing the quality of wind power scenarios for stochastic unit commitment

Didem Sari, Youngrok Lee, Sarah Ryan, David Woodruff
2015 Wind Energy  
To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power.  ...  In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day-ahead unit commitment problems.  ...  Acknowledgment: This material is based on work supported by the ARPA-E initiative of the U.S.  ... 
doi:10.1002/we.1872 fatcat:ajlgn4d5pvcsfeil4gj7mic5m4

Probabilistic Forecasting of Regional Net-load with Conditional Extremes and Gridded NWP [article]

Jethro Browell, Matteo Fasiolo
2021 arXiv   pre-print
We propose a solution based on a best-in-class load forecasting methodology adapted for net-load, and model the tails of predictive distributions with the Generalised Pareto Distribution, allowing its  ...  In a use-case inspired evaluation exercise based on reserve setting, the conditional tails are shown to reduce the overall volume of reserve required to manage a given risk.  ...  The authors thank National Grid ESO for many discussions on forecasting and reserve setting, Graeme Hawker for support accessing GSP data, Ciaran Gilbert for contributions to Prob-Cast [38] , and the  ... 
arXiv:2103.10335v2 fatcat:xtaghjpgh5d6bf3fht5qxwra3e

Review of Deterministic and Probabilistic Wind Power Forecasting: Models, Methods, and Future Research

Ioannis K. Bazionis, Pavlos S. Georgilakis
2021 Electricity  
The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy.  ...  Furthermore, in recent years, in order to observe and study the uncertainty of forecasts, probabilistic forecasting models have been developed in order to give a wider view of the possible prediction outcomes  ...  The work [14] focused on the principals and features of state-of-the-art wind power forecasting uncertainty analysis.  ... 
doi:10.3390/electricity2010002 fatcat:7jm5mqb5o5hpblcqprrrjyv37q

Gaussian Process Operational Curves for Wind Turbine Condition Monitoring

Ravi Pandit, David Infield
2018 Energies  
Abstract: Due to the presence of an abundant resource, wind energy is one of the most promising renewable energy resources for power generation globally, and there is constant need to reduce operation  ...  Three operational curves, namely, the power curve, rotor speed curve and blade pitch angle curve, are constructed using the Gaussian Process approach for continuous monitoring of the performance of a wind  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en11071631 fatcat:vh2ibdny4nbu5es7uln72qk4ai

Uncertainty Quantification Analysis of Wind Power: A Data-driven Monitoring-Forecasting Framework

Wei Wei, Jiang Wu, Yang Yu, Tong Niu, Xinxin Deng
2021 IEEE Access  
However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further  ...  based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating  ...  Finally, the outliers detected by IFA are interpolated using the cubic spline interpolation method to erase their negative effect on probabilistic forecasting modeling. VI.  ... 
doi:10.1109/access.2021.3086583 fatcat:d5p4bhg55nhf5ndj4wn5liebbq

Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

Bo Jing, Zheng Qian, Hamidreza Zareipour, Yan Pei, Anqi Wang
2021 Applied Sciences  
The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment.  ...  This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF).  ...  Reference [13] proved that cubic spline interpolation (CSI) can fit smooth and accurate power curves.  ... 
doi:10.3390/app11073048 fatcat:wxq4y6f5nrfdrim2cgyuzvvmz4

Evaluation of wind power forecasts—An up‐to‐date view

Jakob W. Messner, Pierre Pinson, Jethro Browell, Mathias B. Bjerregård, Irene Schicker
2020 Wind Energy  
3 for a general state-of-the-art report on wind power forecasting or Kariniotakis 4 for a recent coverage of challenges related to wind power forecasting (and extension to other renewable energy sources  ...  Wind power forecast evaluation is of key importance for forecast provider selection, forecast quality control, and model development.  ...  ACKNOWLEDGEMENTS DATA AVAILABILITY STATEMENT All the data associated with this paper, including code to reproduce all the results, can be found in Messner and Browell. 21 ORCID Jakob W.  ... 
doi:10.1002/we.2497 fatcat:wyzbp62kjbgw3fchiofgxgmmbu

Research on Prediction Method of Reasonable Cost Level of Transmission Line Project Based on PCA-LSSVM-KDE

Zhao Xue-hua, Miao Xu-juan, Zhang Zhen-gang, Hao Zheng
2019 Mathematical Problems in Engineering  
Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function  ...  This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost.  ...  distribution polarity curve of the prediction error is fitted by cubic spline interpolation, and the (α/2) and 1 − (α/2) quantile are found.  ... 
doi:10.1155/2019/1649086 fatcat:kppmwrrcljd3fi5zmkorxgtcre

Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

Amanda Lenzi, Pierre Pinson, Line H. Clemmensen, Gilles Guillot
2016 Stochastic environmental research and risk assessment (Print)  
for support.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution  ...  Based on this loose behaviour, we talk interchangeably here about wind speed and wind power.  ... 
doi:10.1007/s00477-016-1329-0 fatcat:voapcia7cng3fhjmqzemtrh62q

An analog ensemble for short-term probabilistic solar power forecast

S. Alessandrini, L. Delle Monache, S. Sperati, G. Cervone
2015 Applied Energy  
The impact of climatology on forecast accuracy is evaluated. consistency, reliability, resolution and skill.  ...  h i g h l i g h t s A novel method for solar power probabilistic forecasting is proposed. The forecast accuracy does not depend on the nominal power.  ...  Acknowledgements This material is based upon work supported by the Department of Energy, under Award Number DOE E0006016.  ... 
doi:10.1016/j.apenergy.2015.08.011 fatcat:mw42duz2lfavlcwyckpgl74dsu

Bivariate Gaussian models for wind vectors in a distributional regression framework

Moritz N. Lang, Georg J. Mayr, Reto Stauffer, Achim Zeileis
2019 Advances in Statistical Climatology, Meteorology and Oceanography  
This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.  ...  them employing flexible regression splines.  ...  This paper was edited by Christopher Paciorek and reviewed by Sebastian Lerch and one anonymous referee.  ... 
doi:10.5194/ascmo-5-115-2019 fatcat:h2gsjdvso5hp7ejrmyj757ssym

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Phathutshedzo Mpfumali, Caston Sigauke, Alphonce Bere, Sophie Mulaudzi
2019 Energies  
A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models.  ...  The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation  ...  Support Vector Regression Support vector regression (SVR) is based on support vector machines (SVM) and uses different kernel functions which map low dimensional data to high dimensional space.  ... 
doi:10.3390/en12183569 fatcat:mniaodno7nec5b6yn6xd25tzqy
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