A new intelligent method based on combination of VMD and ELM for short term wind power forecasting

Abdoos, Ali Akbar

doi:10.1016/j.neucom.2016.03.054

Cited by 217 publications

(94 citation statements)

References 32 publications

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“…On the other hand, the future generated wind power is estimated using previous historical data. The structure of the VMD-LSTM method is depicted in Figure 1, the process of which can be explained as follows [26]:…”

Section: The Whole Process Of the Vmd-lstm Methodsmentioning

confidence: 99%

“…Based on the above-mentioned information, a decomposition method named variational model decomposition (VMD) was introduced for the purpose of improving big multi-step prediction accuracy for hourly day-ahead wind power generation. Unlike EMD, VMD transforms the signal into a non-recursive signal and has a good self-adaptive ability to remove the stochastic volatility [26].…”

Section: Introductionmentioning

confidence: 99%

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Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

et al. 2018

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A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.Energies 2018, 11, 3227 2 of 20 dispatching plan in time, reduce the operating cost of the power system, and determine the appropriate wind power price [4].Wind power forecasts (WPFs) can be divided into four different types: very short-term (a few seconds to 30 min), short-term (30 min to 6 h), medium-term (6 h to 24 h), and long-term (one day and more) [5]. The China National Energy Bureau (NEB) enacted a regulation in 2011 that requires the hourly prediction of day-ahead WPFs for dispatching preparation. In addition, the maximum error of the daily forecast curve should not exceed 25%, and the root mean square error (RMSE) of the all-day forecast results should be less than 20% [6]. Due to the randomness of wind power output forecasts, wind power has brought new demands to the safe operation of the power system. Hence, day-ahead WPFs, especially for WPFs up to 1-24 h, have become a hot button issue in wind power systems and new energy domains with the implementation of large-scale wind power projects [7].The mainstream WPF methods are generally divided into physical methods and machine learning methods [8]. Physical methods aim to describe the physical process of the transformation from wind power to electric energy, and physical models rely on numerical weather prediction (NWP) [9]. Since multiple parameters are involved in NWP, and wind farms are located in sparsely populated regions, complete data can be hardly guaranteed [10]. The statistical methods applied in the WPF field are mostly time-series-based appr...

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Section: The Whole Process Of the Vmd-lstm Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

et al. 2018

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“…To estimate the bandwidth of each mode y i , three main steps can be considered: (i) using Hilbert transform to obtain unilateral frequency spectrum by calculating each mode y i ; (ii) shifting the frequency spectrum of each mode to baseband by mixing an exponential tuned to the respective estimated center frequency; (iii) estimating the bandwidth of each mode y i by making use of the H 1 Gaussian smoothness of the demodulated signal. Therefore, the decomposition process of VMD can be converted into the following optimization problem [40]:…”

Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Wang

2018

Energies

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Abstract:With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.

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“…The bandwidth selected the default value 0. The number of variational mode components is determined by the method of observation center frequency [38]. Set the initial value of to 2.…”

Section: Simulation Signals Analysismentioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

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Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

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A new intelligent method based on combination of VMD and ELM for short term wind power forecasting

Cited by 217 publications

References 32 publications

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

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