A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition

Yin, Hao; Ou, Zuhong; Huang, Shucai; Meng, Anbo

doi:10.1016/j.energy.2019.116316

Cited by 75 publications

(27 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Su et al [99] took wind frequency components and wind turbine status into consideration and proposes a WPD-EEMD-LSTM model for very short-term wind power prediction. Yin et al [94] developed EMD-VMD-CNN-LSTM architecture that effectively utilized the relationship between wind speed, wind energy and wind direction. The method adopted EMD-VMD to process the original data to generate sub-sequences with coupling relationship, utilized CNN-LSTM as a cascade prediction model and finally superimposed all sub-sequence prediction values to output the results.…”

Section: Rnn-based Modelsmentioning

confidence: 99%

A comprehensive review on deep learning approaches in wind forecasting applications

Luo

Yang

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include timeseries-based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto-encoder-based approaches. In addition, future development directions of deep-learning-based wind energy forecasting have also been discussed. K E Y W O R D S deep learning, deep neural networks, learning (artificial intelligence)This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Rnn-based Modelsmentioning

confidence: 99%

A comprehensive review on deep learning approaches in wind forecasting applications

Luo

Yang

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…The artificial neural network has the characteristics of self-adaptive and self-learning, which can deal with complex systems, but it has the problems of slow training speed, difficult to determine the network structure and parameters, and easy to fall into local optimum. In recent years, with the development of deep learning theory, many scholars also apply some deep learning models to the prediction of wind power (Liu et al, 2019; Yin et al, 2019). These models include long short-term memory (Han et al, 2019; Son et al, 2019; Sun et al, 2020), deep belief network (Sun et al, 2018; Wang et al, 2016b; Wang et al, 2019c), convolution neural network (Huang and Kuo, 2019; Ju et al, 2019), recurrent neural network (Olaofe, 2014; Shi et al, 2018; Yona et al, 2009), and so on.…”

Section: The Deterministic Prediction Of Wind Powermentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

View full text Add to dashboard Cite

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

show abstract

“…Zhu et al proposed a multivariate method for ultra-short-term wind power forecasting based on long short-term memory (LSTM) to forecast the ultra-short-term wind power [19]. As the algorithm has its distinct advantages and disadvantages, some works about utilizing hybrid deep learning algorithms were also discussed in [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Huang

Liu

et al. 2021

Energies

View full text Add to dashboard Cite

More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

show abstract

A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition

Cited by 75 publications

References 38 publications

A comprehensive review on deep learning approaches in wind forecasting applications

A comprehensive review on deep learning approaches in wind forecasting applications

A state-of-the-art review on wind power deterministic prediction

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Contact Info

Product

Resources

About