• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
【24h】

Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation

机译：基于自适应神经替代的波能转换器布局优化方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Installed renewable energy capacity has expanded massively in recent years. Wave energy, with its high capacity factors, has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to using just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model's hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs.
机译：近年来，已安装的可再生能源发电量已大大增加。波浪能具有高容量因子，具有巨大的潜力来补充已建立的太阳能和风能资源。这项研究探讨了在数值模拟的海洋环境中，在尺寸受限的农场中优化先进的三束波能量转换器的布局的问题。在波浪场中模拟和计算复杂的水动力相互作用的计算成本可能很高，这限制了优化方法只能使用数千个评估。为了解决这个昂贵的优化问题，提出了一种自适应神经替代优化（ANSO）方法，该方法由经过训练的观察值数量有限的替代递归神经网络（RNN）模型组成。该模型与用于调整模型超参数的快速元启发式优化器结合在一起。使用贪婪的局部搜索和回溯优化策略来应用训练后的模型。为了评估所提出方法的性能，在四种真实波浪情景（悉尼，珀斯，阿德莱德和塔斯马尼亚）中比较了一些更流行和成功的进化算法（EA）。实验结果表明，自适应神经模型在总利用功率输出方面与其他优化方法相比具有竞争优势，而在总计算成本方面则具有更快的竞争力。

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