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
Wind speed time series reconstruction using a hybrid neural genetic approach
Héctor Rodríguez Rangel,Juan J. Flores,Vicenç Puig Cayuela,Luis A. Morales,A Guerra,Felix Calderon +5 more
- 01 Nov 2017
TL;DR: In this work, a hybrid methodology is proposed, and it uses a compact genetic algorithm with an artificial neural network to reconstruct wind speed time series using a ANN defined by a Compact Genetic Algorithm.
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Development of a Neural Network-based Renewable Energy Forecasting Framework for Process Industries
TL;DR: In this article, a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry is presented.
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The impact of short term storage on power system operation
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- 19 May 2015
TL;DR: In this paper, the authors examined peak and off peak benefits realised by installing a short-term discharge storage unit in a system with a high penetration of wind power in 2020, and showed that wind curtailment can be reduced in the storage scenario, with a larger reduction in peak time ramping of gas generators realized.
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The influence of wind direction on short-term wind power prediction: A case study in north China
Ding Yuyu,Hai Zhou,Tan Zhiping,Chen Ying,Jie Ding +4 more
- 21 May 2012
TL;DR: In this paper, a short-term wind power forecasting model is developed based on the data of wind speed, wind direction, wind farm output and numerical weather prediction, which is applied on a wind farm in north China.
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Deep Quantile Regression Based Wind Generation and Demand Forecasts
N. Kirthika,K. I. Ramachandran,Sasi K. Kottayil +2 more
- 13 Dec 2019
TL;DR: The effectiveness of DQR is examined using the low and high seasonal wind and demand datasets and the quantitative comparison of the quality in all the estimated PIs using the proposed method proves to outperform the other state-of-the-art methods.
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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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