Multi‐step wind power forecast based on VMD‐LSTM

Han, Li; Zhang, Rongchang; Wang, Xuesong; Bao, Achun; Jing, Huitian

doi:10.1049/iet-rpg.2018.5781

Cited by 116 publications

(49 citation statements)

References 31 publications

(39 reference statements)

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“…VMD is a completely non-recursive signal decomposition method based on the frequency domain, which to some extent overcomes many shortcomings of EMD. Li et al used VMD to decompose wind power data into long-term modes, wave modes and random modes, which is more conducive for the prediction model to better understand the characteristics of the three constituent modes [23]. With the improvement of wind power prediction on the stability of sample data, data preprocessing has been improved on the original method.…”

Section: Data Preprocessing 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: Data Preprocessing 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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“…Gaussian kernel is suitable for many application scenarios for its smooth characteristics, which is usually considered to be the first choice. In this paper, the Gaussian kernel is applied, and it can be expressed as (15).…”

Section: A Nonparametric Kernel Density Estimationmentioning

confidence: 99%

“…Recently, A large number of new methods based on LSTM for renewable energy and load forecasting are proposed. Han et al [15] proposed a prediction model based on variational mode decomposition and LSTM. Yu et al [16] proposed an enhanced forget-gate LSTM Model.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

et al. 2019

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Wind energy is a kind of sustainable energy with strong uncertainty. With a large amount of wind power injected into the power grid, it will inevitably affect the security, stability and economic operation of the power grid. High-precision wind power spot prediction and fluctuation interval information can provide more adequate decision-making support for grid scheduling and optimization. Hence, this paper proposes a K-Means-long short-term memory (K-Means-LSTM) network model for wind power spot prediction, and a nonparametric kernel density estimation (KDE) model with bandwidth optimization for wind power probabilistic interval prediction. The long short-term memory (LSTM) network has a strong memory function, which can establish the correlation between the data before and after, so as to improve the prediction accuracy. The K-Means clustering method forms different clusters of wind power impact factors to generate a new LSTM sub-prediction model. The optimization of the bandwidth in the nonparametric KDE is implemented by the mean integrated squared error criterion. In addition, a part of the dataset is deliberately demarcated from the wind power historical dataset to generate reasonable wind power prediction errors. The simulation results show that the proposed K-Means-LSTM network model has higher prediction accuracy than the back propagation (BP) neural networks, Elman neural networks, support vector regression (SVR) and LSTM network models. Compared with the KDE model with random bandwidth and the Gaussian distribution model, the bandwidth optimization model proposed in this paper has more narrow prediction intervals with higher interval coverage rates. INDEX TERMS Wind power prediction, Kernel density estimation, long short-term memory, K-means clustering, probabilistic interval prediction.

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“…Shao et al [6] also applied MCSCNN-LSTM with a -step strategy for multistep power consumption forecasting. Han et al [20] proposed variational mode decomposition-LSTM for multi-step wind power forecasting with a direct rolling strategy. Deng et al [21] applied multi-scale CNN considering time variables for multistep short-term loading forecasting.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Shao

Kim

2020

IEEE Access

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Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost. However, most of the current research only focuses on one-step STLF, which cannot satisfy the human-beings needs. Besides, short-term consumption fluctuates significantly in different periods and people, which increases the difficulty of forecasting. In this paper, we present a novel deep model named multi-channel long short-term memory (LSTM) with time location (TL-MCLSTM) in a multiple output strategy to forecast the multi-step short-term power consumption. The proposed model contains three channels: power consumption, time location, and customer behavior channels, respectively. Power consumption channel reflects the change and general trend of use; Time location channel reflects the hidden pattern of customer habits, which records the information consisting of time, day of the week, holidays. Moreover, we combine a convolution autoencoder and k-means to identify the type of behavior at the customer behavior channel. Power consumption and time location channels are trained individually through the LSTM as it has excellent memory function. Extracted features from LSTM in power consumption and time location channels are combined with customer behavior as comprehensive features to forecast. We designed, trained, and verified our proposed deep model on two nature data sets, and compared with other leading deep learning-based methods. The comparative studies have confirmed the effectiveness and priority of TL-MCLSTM for multi-step short-term consumption forecasting. INDEX TERMS Short-term Load Forecasting (STLF), smart grid, LSTM, multi-step forecasting, convolution autoencoder.

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Multi‐step wind power forecast based on VMD‐LSTM

Cited by 116 publications

References 31 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

Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

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