Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction

Liu, Ming-De; Ding, Lei; Bai, Yulong

doi:10.1016/j.enconman.2021.113917

Cited by 223 publications

(63 citation statements)

References 43 publications

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“…NWP is to determine the initial conditions and boundary conditions according to the actual atmospheric conditions, and then use mathematical calculation to solve the thermodynamic equation and hydrodynamic equation during the weather change period, so as to predict the future weather (Castorrini et al, 2020). Time series model has a good advantage in dealing with stable linear time series, and can get good prediction results (Erdem and Shi, 2011;Liu et al, 2021;Jiang et al, 2018). Because of the instability and randomness of wind speed series, time series method is poor in wind speed prediction, and cannot show the fluctuation and irregular characteristics of wind speed.…”

Section: Gamentioning

confidence: 99%

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Zhang

Chen

2021

Environ Sci Pollut Res

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In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

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Section: Gamentioning

confidence: 99%

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Zhang

Chen

2021

Environ Sci Pollut Res

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“…e fusion combination is optimized by other prediction methods in different prediction stages, including input data stabilization, model parameter optimization, and output error correction. Based on empirical mode decomposition (EMD) [22][23][24][25], variational mode decomposition (VMD) [26][27][28][29], analytical mode decomposition (AMD) [30,31], the wavelet decomposition [14,25,32], and so on, the wind speed sequence data was preprocessed to make the data stable. Better prediction results are achieved.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Shao

Song

Bian

et al. 2021

Advances in Materials Science and Engineering

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Wind energy is a renewable energy source with great development potential, and a reliable and accurate prediction of wind speed is the basis for the effective utilization of wind energy. Aiming at hyperparameter optimization in a combined forecasting method, a wind speed prediction model based on the long short-term memory (LSTM) neural network optimized by the firework algorithm (FWA) is proposed. Focusing on the real-time sudden change and dependence of wind speed data, a wind speed prediction model based on LSTM is established, and FWA is used to optimize the hyperparameters of the model so that the model can set parameters adaptively. Then, the optimized model is compared with the wind speed prediction based on other deep neural architectures and regression models in experiments, and the results show that the wind speed model based on FWA-improved LSTM reduces the prediction error when compared with other wind speed prediction-based regression methods and obtains higher prediction accuracy than other deep neural architectures.

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“…The algorithms in the field of time series prediction can be divided into two categories: traditional machine learning algorithms and deep learning algorithms. Traditional machine learning algorithms are mostly based on statistical models, Current popular algorithms include Autoregressive Moving Average (ARIMA) (Liu et al, 2021), support vector machine (SVM) (Ma et al, 2018), Regression Tree (Yang et al, 2020b), Random Forest (Li et al, 2021b), and artificial neural networks (ANN) (Wei et al, 2019;Bui et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption

Gao

Qian

et al. 2021

Front. Energy Res.

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Predicting system energy consumption accurately and adjusting dynamic operating parameters of the HVAC system in advance is the basis of realizing the model predictive control (MPC). In recent years, the LSTM network had made remarkable achievements in the field of load forecasting. This paper aimed to evaluate the potential of using an attentional-based LSTM network (A-LSTM) to predict HVAC energy consumption in practical applications. To evaluate the application potential of the A-LSTM model in real cases, the training set and test set used in experiments are the real energy consumption data collected by Kitakyushu Science Research Park in Japan. Pearce analysis was first carried out on the source data set and built the target database. Then five baseline models (A-LSTM, LSTM, RNN, DNN, and SVR) were built. Besides, to optimize the super parameters of the model, the Tree-structured of Parzen Estimators (TPE) algorithm was introduced. Finally, the applications are performed on the target database, and the results are analyzed from multiple perspectives, including model comparisons on different sizes of the training set, model comparisons on different system operation modes, graphical examination, etc. The results showed that the performance of the A-LSTM model was better than other baseline models, it could provide accurate and reliable hourly forecasting for HVAC energy consumption.

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Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction

Cited by 223 publications

References 43 publications

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Potential Analysis of the Attention-Based LSTM Model in Ultra-Short-Term Forecasting of Building HVAC Energy Consumption

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