• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Quality Control, Transactions >Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine
【24h】

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

机译：基于SALP Swarm算法的超短期风电预测优化极端学习机

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy.
机译：风力发电占电网比例的增加，因此高效，准确的实时风力电力预测对于风电网尤为重要。鉴于风的强烈随机性和风力波动以及预测风力的难度，提出了一种SALP群算法 - 非常学习机（SSA-ELM）的超短术风功率预测模型。在这种情况下，多输入样品集由历史风速，温度，风向，大气压和与风力高度相关的其他气候因子组成，并且在训练过程中确定网络参数。为了提高预测模型的适应性和准确性，通过在迭代过程中探索和开发SALP群算法来优化极端学习机（ELM）的输入权重矩阵和隐藏层偏差。最后，仿真实验是用河南省风电场的实际数据进行的，以及与传统的极端学习机，粒子群优化极端学习机（PSO-ELM）和后传播（BP）神经网络模型的比较新方法避免落入局部极值，并具有更快的收敛速度和更高的预测精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号