Journal Article10.1016/J.RENENE.2011.05.033
Current methods and advances in forecasting of wind power generation
TL;DR: A review of the current methods and advances in wind power forecasting and prediction can be found in this article, where numerical wind power prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed.
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About: This article is published in Renewable Energy. The article was published on 01 Jan 2012. The article focuses on the topics: Wind power forecasting & Wind power.
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Citations
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
TL;DR: This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting, which is based on the auxiliary layer, forecasting base layer, and core layer and aims to search for the optimal solutions under constraints for optimizing the key parameters of the prediction models.
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Short-term wind speed and power forecasting using an ensemble of mixture density neural networks
TL;DR: An application of the proposed approach to a data set of the measured wind speed and power from an operational wind turbine in a wind farm in Taiwan is used to test the methodology and demonstrates that the proposed methodology works well for the multi-step ahead windspeed and power forecasting.
125
Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
Song Li,Peng Wang,Lalit Goel +2 more
TL;DR: A novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm to overcome the nonstationarity of wind power series and improve the forecasting accuracy.
Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming
TL;DR: In this article, the authors identified the tradeoff effects of hydropower and wind power integrated operation by establishing a framework of coupling models, where a martingale model that captures the evolution of forecasting uncertainty was used to generate synthetic scenarios of uncertain load demand.
123
References
A Description of the Advanced Research WRF Version 3
C. Skamarock,B. Klemp,Jimy Dudhia,O. Gill,Dale Barker,G. Duda,Xiang-Yu Huang,Wei Wang,G. Powers +8 more
- 01 Jan 2008
TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
A Description of the Advanced Research WRF Version 2
William C. Skamarock,Joseph B. Klemp,Jimy Dudhia,David O. Gill,Dale Barker,Wei Wang,Jordan G. Powers +6 more
- 01 Jun 2005
TL;DR: The Weather Research and Forecasting (WRF) model as mentioned in this paper was developed as a collaborative effort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (F
The Use of Model Output Statistics (MOS) in Objective Weather Forecasting
Harry R. Glahn,Dale A. Lowry +1 more
TL;DR: Model Output Statistics (MOS) as mentioned in this paper is an objective weather forecasting technique which consists of determining a statistical relationship between a predictand and variables forecast by a numerical model at some projection time(s).
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