A Review of Wind Power Forecasting Models
TLDR
In this paper, a review on comparative analysis on the foremost forecasting models, associated with wind speed and power, based on physical methods, statistical methods, hybrid methods over different time-scales.About:
This article is published in Energy Procedia.The article was published on 2011-01-01 and is currently open access. It has received 337 citations till now. The article focuses on the topics: Wind power forecasting & Wind power.read more
Citations
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A review of combined approaches for prediction of short-term wind speed and power
TL;DR: In this article, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance, and an up-to-date annotated bibliography of the wind forecasting literature is presented.
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A Literature Review of Wind Forecasting Methods
TL;DR: In this article, an overview of new and current developments in wind forecasting is given where the focus lies upon principles and practical implementations, and the authors give a literature survey on the categories and major methods of wind forecasting.
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Wind power forecasting based on daily wind speed data using machine learning algorithms
TL;DR: It is demonstrated that machine learning algorithms could be successfully used before the establishment of wind plants in an unknown geographical location whether it is logical by using the model of a base location or not.
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Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts
TL;DR: In this paper, two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms are investigated.
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Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information
Gerardo J. Osorio,João C. O. Matias,Joao P. S. Catalao,Joao P. S. Catalao,Joao P. S. Catalao +4 more
TL;DR: A new hybrid evolutionary-adaptive methodology for wind power forecasting in the short-term is proposed, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system.
References
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Day-ahead wind speed forecasting using f-ARIMA models
TL;DR: In this article, the authors examined the use of fractional-ARIMA or f-ARAMA models to model, and forecast wind speeds on the day-ahead and two-day-ahead (48 h) horizons.
The state-of-the-art in short-term prediction of wind power. A literature overview
TL;DR: In this paper, the authors present the state of the art in wind power forecasting using ANEMOS.plus (Advanced Tools for the Management of Electricity Grids with Large-Scale Wind Generation) and SafeWind projects.
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Large-scale integration of wind power into different energy systems
TL;DR: In this paper, the ability of different energy systems and regulation strategies to integrate wind power is expressed by the following three factors: the degree of electricity excess production caused by fluctuations in wind and Combined Heat and Power (CHP) heat demands, the ability to utilise wind power to reduce CO2 emission in the system, and the benefit from exchange of electricity on the market.
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Long-term wind speed and power forecasting using local recurrent neural network models
TL;DR: Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.
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Climate change impacts on wind energy: A review
TL;DR: In this paper, the authors review possible mechanisms by which global climate variability and change may influence the wind energy resource and operating conditions, summarize some of the tools that are being employed to quantify these effects and the sources of uncertainty in making such projections, and discuss results of studies conducted to date.