.png){kind=icon}
返回
A short-term wind power forecasting method based on multivariate signal decomposition and variable selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apenergy.2024.122759.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate and effective short-term wind power forecasting is vital for the large-scale integration of wind power generation into the power grid. However, due to the intermittence and volatility of wind resources, short-term wind power forecasting is challenging. To address the issue that the existing decomposition forecasting methods ignore the coupling relationship between wind power series and multiple meteorological series, this study proposes a short-term wind power forecasting method based on multivariate signal decomposition and variable selection. First, multivariate variational mode decomposition (MVMD) is used to perform time-frequency synchronous analysis on wind power and multidimensional meteorological series, thereby decomposing them into the same predefined number of frequency-aligned intrinsic mode functions (IMFs). Secondly, elastic net (EN) is used for supervised variable selection on all IMFs to provide a high-quality training set for the forecasting model, thereby enhancing precision and interpretability. Next, a hybrid deep neural network combining convolutional neural network (CNN), bidirectional long-short term memory (BiLSTM) neural network, and multi-head attention (MHA) mechanism is employed to model the output curve of a group of wind turbines in a wind farm. Finally, the proposed method is comprehensively evaluated through four sets of comparative experiments and multiple evaluation metrics on data gathered from the Mahuangshan first wind farm in China with four forecasting horizons: 15-min ahead, 30-min ahead, 45-min ahead, and 1-h ahead. The experimental results show that the proposed method significantly outperforms fifteen existing deep learning methods in terms of precision and stability.
Keyword:
Short-term wind power forecasting
Decomposition forecasting method
Multivariate variational mode decomposition
Variable selection
Multi -head attention mechanism
期刊
IF:
11
论文数:
2.6W
被引数:
17.8W
机构
引用论文
Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps基于变分模态分解和高阶模糊认知图的风电功率预测
PREAI
分享收藏Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting.png)
RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏Effects of oral administration of a synthetic fragment of human growth hormone on lipid metabolism
OAAI
OAAI分享收藏Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awarenessAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition基于时态卷积神经网络和变分模态分解的风电功率短期预测方法研究SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF基于emd-pca-rf改进LSTM的风电功率预测SUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
OAAI分享收藏A wind power forecasting method based on optimized decomposition prediction and error correction基于优化分解预测和误差修正的风电功率预测方法
PREAI
分享收藏
查看更多内容