A novel prediction model for wind power based on improved long short-term memory neural network

Wang, Jianbo; Zhu, Hongqiu; Zhang, Yingjie; Cheng, Fei; Zhou, Chi

doi:10.1016/j.energy.2022.126283

Cited by 45 publications

(21 citation statements)

References 38 publications

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“…In addition, a new integrated forecasting model for wind power is presented by replacing the BP network with light gradient boosting machine (LGBM) and random forest (RF), combined with wavelet decomposition. In deep learning models, LSTM networks are prominent in handling time series forecasting problems owing to their unique structure [23]- [26]. Therefore, based on LSTM, single-step wind speed forecasting model and a multi-step wind speed forecasting model are established.…”

Section: A Related Workmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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The escalating incorporation of wind energy into power grids imposes constraints on the effective operation and control of power systems. Effective short-term wind power forecasting technology is essential for safe power supply and stable operation of the power grid. Thus, a UMAP-IVMD-ILSTM model for wind power forecasting is proposed based on the uniform manifold approximation and projection (UMAP) integrated improved variational mode decomposition (IVMD) and multi-level improved long and short-term memory (LSTM) networks. First, the UMAP approach was utilized to reduce the dimension of related meteorological indicators, and k-means clustering was applied to classify the weather categories after dimension reduction. Subsequently, through the improved variational mode decomposition, the wind power of the adjacent day is decomposed. Moreover, long and short-term memory networks were optimized using the sparrow search algorithm (SSA) as an improved LSTM (ILSTM), and a wind power forecasting model with multi-level LSTM was established for each mode, and the forecasting results of each mode were integrated. Through actual wind farm operation data, it was proven that the method proposed in this study is effective in processing high-dimensional numerical weather prediction (NWP) data. The model can better preserve the information of the original data and reduce the complexity of the original data sequence based on visualization. Compared with the comparative models, the proposed model reduces the RMSE and MAE by up to 18.2% and 21.6% respectively, and has the least running time, which can effectively improve the accuracy and efficiency of wind power prediction.

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Section: A Related Workmentioning

confidence: 99%

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

Zhang,

2024

IEEE Access

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“…LSTM neural networks are used to compensate for the inability of recurrent neural networks (RNNs) to handle long-term dependencies [12][13][14]. They can be used to solve many problems that require capturing long-term dependencies, and have been widely used in speech synthesis [15,16], audio analysis [17,18] and video captioning [19][20][21].…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates

Zhu

Pang

Zhou

et al. 2023

Meas. Sci. Technol.

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Copper is an important resource of non-ferrous metals. Electrolytic refining is one of the main methods to produce fine copper. In electrolytic process, plate conditions seriously affects the output and quality of copper. Timely and accurate prediction of the working condition of the plate is of great significance to the copper electrolytic refining process. Aiming at the problems of the traditional plate conditions detection algorithm with large lag, poor anti-interference ability and low accuracy, this paper proposes a plate condition prediction model based on LSTM with attention mechanism. The average gray value of the plate in the infrared image is used to characterize the plate working condition. In hidden layer, double layer LSTM network is used to improve the effect of model training. A unique attention mechanism is added to learn the correlation between input and output better and improve the accuracy of model prediction. Experimental results show that the accuracy of the proposed model for plate condition prediction reaches 95.11%. Compared with the commonly used LSTM algorithm, the plate condition prediction model proposed in this paper has stronger prediction ability.

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“…The power output of a wind power generation system has great randomness, volatility and uncertainty, so its large-scale integration into the power grid brings great challenges to the safe and stable operation of the power system [ 1 , 2 ]. Soft sensor technology establishes a mathematical model between the auxiliary and target variables to predict the target variables, which has the characteristics of low cost and high accuracy [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors

Lei,

Ma,

Zhu

et al. 2024

Sensors

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Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

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A novel prediction model for wind power based on improved long short-term memory neural network

Cited by 45 publications

References 38 publications

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

An Integrated Modeling Strategy for Wind Power Forecasting Based on Dynamic Meteorological Visualization

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors

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