Wind power forecasting using wavelet transforms and neural networks with tapped delay

Saroha, Sumit; Aggarwal, Shubhani

doi:10.17775/cseejpes.2016.00970

Cited by 39 publications

(14 citation statements)

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“…One important solution to manage wind variability is to provide accurate wind power forecasting so that dispatchers can schedule countermeasures and adjust the maintenance plans in time [6].…”

Section: () Exmentioning

confidence: 99%

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Xiang

Zhang

et al. 2021

Applied Energy

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Section: () Exmentioning

confidence: 99%

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Xiang

Zhang

et al. 2021

Applied Energy

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“…In recent years, there have been some new ideas in this research field. For example, in literatures (Tascikaraoglu et al, 2016;Lixin & Lei, 2018;Saroha & Aggarwal, 2018), wavelet transform was used to decompose the power series to form multiple new subseries that would be predicted in turn. And then the results were combined.…”

Section: Machine Learning Methods In Wppmentioning

confidence: 99%

Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space

Yu¹,

Liu²,

Li³

et al. 2019

Applied Energy

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Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In this paper, a new kind of feature that can describe the process of temporal and spatial variation is proposed, namely, Spatio-Temporal Features. We first map the data collected at each moment from the wind turbine to the plane to form the state map, namely, the scene, according to the relative positions. The scene time series over a period of time is a multi-channel image, i.e. the Spatio-Temporal Features. Based on the Spatio-Temporal Features, the deep convolutional network is applied to predict the wind power, achieving a far better accuracy than the existing methods. Compared with the starge-of-the-art method, the mean-square error (M SE) in our method is reduced by 49.83%, and the average time cost for training models can be shortened by a factor of more than 150.

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“…Or in simple terms, for a finite length of series with finite decomposition is (Saroha and Aggarwal 2018 ): …”

Section: Theoretical Backgroundmentioning

confidence: 99%

An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network

2021

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Wind power forecasting using wavelet transforms and neural networks with tapped delay

Cited by 39 publications

References 0 publications

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction

Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space

An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network

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