.png){kind=icon}
返回
A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.energy.2023.129604.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate wind speed forecasting is capable of increasing the stability of wind power system. Notably, there are numerous factors affecting wind speed, thus causing wind speed forecasting to be difficult. To address the above -mentioned challenge, a novel hybrid model integrating genetic algorithm (GA), variational mode decomposition (VMD), improved dung beetle optimization algorithm (IDBO), and Bidirectional long short-term memory network based on attention mechanism (BiLSTM-A) is proposed in this study to achieve satisfactory forecasting performance. In the proposed model, GA is adopted to optimize the VMD to eliminate noise and extract original series attributes. And the IDBO is adopted for hyperparameters selection for the BiLSTM-A. The proposed GA-VMD-IDBO-BiLSTM-A is compared with nine established comparable models, with the aim of verifying its forecasting performance. A series of experiments on four 1 -hour real wind series in Stratford are performed to assess the performance of the model. The MAPE of the four datasets forecasting results reached 1.4%, 2.4%, 3.5%, 2.4%. As indicated by the experimental results, GA-VMD can better process the data and improve the forecasting accuracy. IDBO can optimize the parameters of BiLSTM model and improve the forecasting performance. The dual -optimization wind speed forecasting model can obtain high accuracy and strong stability.
Keyword:
Wind speed forecasting
Dung beetle optimization algorithm
Variational mode decomposition
Bidirectional long short-term memory network
Attention mechanism
期刊
IF:
9.4
论文数:
4.3W
被引数:
20.2W
机构
引用论文
One day ahead wind speed forecasting: A resampling-based approach提前一天风速预测: 一种基于重采样的方法.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏分享收藏
Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China利用混合模型对中国新疆三个不同地点进行中期风速预测RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model基于数据驱动和混合深度学习模型的超短期风电功率点与区间预测ENERGY
ENERGYIF9.4
PREAI
分享收藏Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output通过数值天气预报模型输出的卡尔曼滤波进行风速和风能预测APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏
查看更多内容