Journal ArticleDOI
On comparing three artificial neural networks for wind speed forecasting
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TLDR
A comprehensive comparison study on the application of different artificial neural networks in 1-h-ahead wind speed forecasting shows that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics.About:
This article is published in Applied Energy.The article was published on 2010-07-01. It has received 636 citations till now. The article focuses on the topics: Wind speed & Mean absolute percentage error.read more
Citations
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Proceedings ArticleDOI
A review of wind power and wind speed forecasting methods with different time horizons
TL;DR: In this article, the main challenges and problems associated with wind power prediction are discussed, and an overview of comparative analysis of various available forecasting techniques is discussed as well as a major challenges and major challenges.
Journal ArticleDOI
ARMA based approaches for forecasting the tuple of wind speed and direction
Ergin Erdem,Jing Shi +1 more
TL;DR: In this paper, four approaches based on autoregressive moving average (ARMA) method are employed for short-term forecasting of wind speed and direction are employed to forecast wind turbine operation and efficient energy harvesting.
Journal ArticleDOI
Current status and future advances for wind speed and power forecasting
Jaesung Jung,Robert Broadwater +1 more
TL;DR: An overview of existing research on wind speed and power forecasting can be found in this article, where state-of-the-art approaches for wind power and wind speed forecasting are discussed.
Journal ArticleDOI
Deep learning based ensemble approach for probabilistic wind power forecasting
TL;DR: The proposed ensemble approach has been extensively assessed using real wind farm data from China, and the results demonstrate that the uncertainties in wind power data can be better learned using the proposed approach and that a competitive performance is obtained.
Journal ArticleDOI
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.
References
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Journal ArticleDOI
Introduction to Time Series and Forecasting.
TL;DR: A general approach to Time Series Modelling and ModeLLing with ARMA Processes, which describes the development of a Stationary Process in Terms of Infinitely Many Past Values and the Autocorrelation Function.
Book
Introduction to time series and forecasting
TL;DR: In this paper, the authors present a general approach to time series analysis based on simple time series models and the Autocorrelation Function (AFF) and the Wold Decomposition.
Journal ArticleDOI
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Bernard Widrow,Michael A. Lehr +1 more
TL;DR: The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described.
Journal ArticleDOI
A review on the forecasting of wind speed and generated power
TL;DR: A bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting and further direction for additional research and application is proposed.
Journal ArticleDOI
Bayesian model selection and model averaging
TL;DR: This paper reviews the Bayesian approach to model selection and model averaging and emphasizes objective Bayesian methods based on noninformative priors.
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ARMA based approaches for forecasting the tuple of wind speed and direction
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