LSTM-EFG for wind power forecasting based on sequential correlation features

Yu, Ruiguo; Gao, Jie; Yu, Mo; Lu, Wenhuan; Xu, Tianyi; Zhao, Mankun; Zhang, Jie; Zhang, Ruixuan; Zhang, Zhuo

doi:10.1016/j.future.2018.09.054

Cited by 241 publications

(63 citation statements)

References 13 publications

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“…Srivastava and Lessmann (2018) applied an LSTM model to predict solar energy and concluded that this is a reliable prediction model. Yu et al (2019) used a combined LSTM-EFG model to predict wind speed.…”

Section: Prediction Methods For Renewable Energymentioning

confidence: 99%

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

et al. 2020

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In response to climate change and continued reliance on traditional high-carbon fossil fuels, promoting the transition toward sustainable energy systems by development of low-carbon energy resources has been seen as the main strategy for mitigating and solving global climate change. However, the promotion of low-carbon energy also faces material supply risks. To provide a reference for the steady and rapid development of renewable energy and other energy in the future energy market, this paper considers renewable energy prediction based on a long-and short-term memory network model as well as the growth rate changes of crude oil, natural gas, nuclear energy, financial revenues, and expenditure. In the prediction process, it is found that natural gas will be a strong competitor for the development of renewable energy in the future. When natural gas grows too quickly, the growth of renewable energy will be negative. On the other hand, when the monthly growth rate of natural gas and crude oil is smaller than that of nuclear energy, renewable energy will display a growth trend, and the rate will increase with the growth of natural gas and nuclear energy. What is more, wind and solar energy will be limited by metallic materials, such as Dy, Nd, Te, and In. Improving the energy density of metals plays a key role in China's transition to a low-carbon energy structure.

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Section: Prediction Methods For Renewable Energymentioning

confidence: 99%

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

et al. 2020

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“…Yu et al proposed the Long Short-Term Memory and Enhanced Forget-Gate network model (LSTM-EFG), which can be used for wind power prediction. Based on correlation, the characteristic data of units within a certain distance are filtered, and the effect of wind power prediction is optimized by cluster analysis [39]. Lin.…”

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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“…In the past decades, experts and scholars have made systematic and effective research on traditional deterministic and probabilistic STLF and VSTLF. Deterministic forecasting methods can be divided 2 of 17 into two main categories [3]: The first category uses statistical forecasting models, such as linear regression [4], curve extrapolation [5], Autoregressive Integrated Moving Average (ARIMA) model [6,7], and other time series methods; the second category uses artificial intelligent forecasting models, such as Bayesian estimation [8], Random Forests [9], Support Vector Regression (SVR) [10,11], Artificial Neural Network (ANN) [12,13], Deep Belief Network (DBN) [14,15], and Long Short Term Memory (LSTM) Network [16,17]. These methods have achieved high forecasting accuracy and good robustness in day-ahead and hour-ahead load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Shi

Mei

et al. 2019

Energies

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With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

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LSTM-EFG for wind power forecasting based on sequential correlation features

Cited by 241 publications

References 13 publications

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

Renewable Energy and Material Supply Risks: a Predictive Analysis Based on An LSTM Model

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

Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

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