Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal

Qin, Yong; Li, Kun; Liang, Zhanhao; Lee, Brendan; Zhang, Fuyong; Gu, Yongcheng; Zhang, Lei; Wu, Fengzhi; Rodriguez, Dragan

doi:10.1016/j.apenergy.2018.11.063

Cited by 108 publications

(22 citation statements)

References 32 publications

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“…Through comparison, selection and improvement of the models, more accurate prediction models are obtained. The modeling methods mainly include Autoregressive models (AR) [24,25], Time Series Models [26,27], Support Vector Machine (SVM) [28,29], Artificial Neural Networks (ANN) [30,31], etc. The initial application of these prediction models in the field of wind power prediction has improved the accuracy of the prediction to a certain extent.…”

Section: Prediction Modelsmentioning

confidence: 99%

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Liu

Zhao

et al. 2020

Energies

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Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

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Section: Prediction Modelsmentioning

confidence: 99%

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Liu

Zhao

et al. 2020

Energies

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“…In this section, the multi-step rolling forecast performance of VMD-LSTM are analysed and compared with several forecast methods, including Persistence (PER) [28], Wavelet (WT) [18] and BP neural network. By using these methods, 4, 8 and 12 steps ahead, forecast is performed, respectively, according to the season.…”

Section: Analysis Of Multi-step Forecast Performance 431 Multi-stepmentioning

confidence: 99%

“…At first, LSTM has achieved good application in the field of speech recognition [14], and then was introduced to forecast wind speed or wind power [15][16][17]. In recent years, many researchers are focused on hybrid forecast models with LSTM, such as the forecasting model based on LSTM network and deep learning neural network [18], the multi-task convolutional LSTM model [19], the smart multi-step deep learning model [20], the hybrid wind speed forecasting model using the LSTM network, hysteretic ELM and differential evolution algorithm [16] and so on. When learning the wind power signal, the LSTM network needs to take into account all the patterns in the signal at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi‐step wind power forecast based on VMD‐LSTM

Han

Zhang

Wang

et al. 2019

IET Renewable Power Generation

110

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To improve the accuracy of multi-step wind power forecast, a variational mode decomposition-long short-term memory (VMD-LSTM) forecast method is proposed. Firstly, the variational mode decomposition method is adopted to decompose the wind power data into three constituent modes, named as the long-term component, the fluctuation component and the random component. Secondly, long short-term memory network is utilised to deeply learn the characteristics of the three constituent modes. Profit from its unique forget gate and memory gate structure, the association with long-term time series is learned to build a multi-step forecast model. Finally, the wind power data from ELIA and NERL are used to test. The error analysis shows that the proposed method has superior performance in the multi-step forecast and real-time forecast.

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“…Due to the worldwide importance of energy resources and price predictions, neural networks and deep learning have received increasing attention over the years, considering their usefulness for energy consumers and generators in relevant decision-making processes. The application of ANN in the energy market cover such a wide range of use cases as wind turbine signal assessment (Qin et al 2019), day-ahead photovoltaic power forecasting (Wang et al 2019a), or crude oil forecasting (Cen and Wang 2019;, to mention a few.…”

Section: Related Workmentioning

confidence: 99%

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

et al. 2019

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Flow-based Market Coupling (FBMC) provides welfare gains from cross-border electricity trading by efficiently providing coupling capacity between bidding zones. In the coupled markets of Central Western Europe, common regulations define the FBMC methods, but transmission system operators keep some degrees of freedom in parts of the capacity calculation. Besides, many influencing factors define the flow-based capacity domain, making it difficult to fundamentally model the capacity calculation and to derive reliable forecasts from it. In light of this challenge, the given contribution reports findings from the attempt to model the capacity domain in FBMC by applying Artificial Neural Networks (ANN). As target values, the Maximum Bilateral Exchanges (MAXBEX) have been chosen. Only publicly available data has been used as inputs to make the approach reproducible for any market participant. It is observed that the forecast derived from the ANN yields similar results to a simple carry-forward method for a one-hour forecast, whereas for a longer-term forecast, up to twelve hours ahead, the network outperforms this trivial approach. Nevertheless, the overall low accuracy of the prediction strongly suggests that a more detailed understanding of the structure and evolution of the flow-based capacity domain and its relation to the underlying market and infrastructure characteristics is needed to allow market participants to derive robust forecasts of FMBC parameters.

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Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal

Cited by 108 publications

References 32 publications

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Multi‐step wind power forecast based on VMD‐LSTM

Forecasting cross-border power transmission capacities in Central Western Europe using artificial neural networks

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