Journal ArticleDOI
A review of combined approaches for prediction of short-term wind speed and power
TLDR
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.Abstract:
With the continuous increase of wind power penetration in power systems, the problems caused by the volatile nature of wind speed and its occurrence in the system operations such as scheduling and dispatching have drawn attention of system operators, utilities and researchers towards the state-of-the-art wind speed and power forecasting methods These methods have the required capability of reducing the influence of the intermittent wind power on system operations as well as of harvesting the wind energy effectively In this context, combining different methodologies in order to circumvent the challenging model selection and take advantage of the unique strength of plausible models have recently emerged as a promising research area Therefore, a comprehensive research about the combined models is called on for how these models are constructed and affect the forecasting performance Aiming to fill the mentioned research gap, this paper outlines the combined forecasting approaches and presents an up-to date annotated bibliography of the wind forecasting literature Furthermore, the paper also points out the possible further research directions of combined techniques so as to help the researchers in the field develop more effective wind speed and power forecasting methodsread more
Citations
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Journal ArticleDOI
A review of deep learning for renewable energy forecasting
TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.
Journal ArticleDOI
Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network
TL;DR: A novel hybrid deep-learning wind speed prediction model, which combines the empirical wavelet transformation and two kinds of recurrent neural network, is proposed, which indicates that the proposed model has satisfactory performance in the high-precision wind speed Prediction.
Journal ArticleDOI
Wind power prediction using deep neural network based meta regression and transfer learning
TL;DR: The effectiveness of the proposed, DNN-MRT technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques.
Journal ArticleDOI
Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
TL;DR: The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term Wind speed forecasting, and Statistical tests of experimental results compared with other popular prediction models demonstrated the proposal can achieve a better forecasting performance.
Journal ArticleDOI
Hour-ahead wind power forecast based on random forests
TL;DR: In this paper, a random forest method is proposed to build an hour-ahead wind power predictor, which is based on spatially averaged wind speed and wind direction, and the random forest does not need to be tuned or optimized, unlike most other learning machines.
References
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Journal ArticleDOI
Time series forecasting using a hybrid ARIMA and neural network model
TL;DR: Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.
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
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.
Journal ArticleDOI
Security-Constrained Unit Commitment With Volatile Wind Power Generation
TL;DR: In this article, a security-constrained unit commitment (SCUC) algorithm is proposed for managing the security of power system operation by taking into account the intermittency and volatility of wind power generation.
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