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首页> 外文期刊>Nordic hydrology >Augmentation of an artificial neural network and modified stochastic dynamic programing model for optimal release policy
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Augmentation of an artificial neural network and modified stochastic dynamic programing model for optimal release policy

机译：改进的人工神经网络和改进的随机动态规划模型，用于最优释放策略

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摘要

In this paper, a comprehensive modified stochastic dynamic programing with artificial neural network (MSDP-ANN) model is developed and applied to derive optimal operational strategies for a reservoir. Most water resource problems involve uncertainty. To show that the msdp-ann model addresses uncertainty in the input variable, the result of the MSDP-ANN model is compared with the performance of a detailed conventional stochastic dynamic programing with regression analysis (CSDP-RA) model. The computational time of the CSDP-ANN model is modified with concave objective functions by deriving a monotonic relationship between the reservoir storage and optimal release decision, and an algorithm is proposed to improve the computational efficiency of reservoir operation. Various indices (i.e. reliability, vulnerability, and resiliency) were calculated to assess the model performance. After comparing the performance of the CSDP-RA model with that of the MSDP-ANN model, it was observed that the msdp-ann model produces a more reliable and resilient model and a smaller supply deficit. Thus, it can be concluded that the MSDP-ANN model performs better than the CSDP-RA model in deriving the optimal operating policy for the reservoir.
机译：本文提出了一种基于人工神经网络（MSDP-ANN）的综合改进的随机动态规划模型，并将其应用到水库的最优调度策略中。大多数水资源问题都涉及不确定性。为了表明msdp-ann模型解决了输入变量中的不确定性，将MSDP-ANN模型的结果与详细的常规随机动态规划与回归分析（CSDP-RA）模型的性能进行了比较。通过推导储层储量与最优释放决策之间的单调关系，利用凹目标函数修正CSDP-ANN模型的计算时间，提出了提高储层调度计算效率的算法。计算了各种指标（即可靠性，脆弱性和弹性）以评估模型性能。在将CSDP-RA模型的性能与MSDP-ANN模型的性能进行比较之后，可以观察到msdp-ann模型产生了更可靠，更具弹性的模型以及较小的供应赤字。因此，可以得出结论，在推导储层的最佳运行策略时，MSDP-ANN模型的性能优于CSDP-RA模型。

著录项

  • 来源
    《Nordic hydrology》 |2015年第5期|689-704|共16页
  • 作者

    Sabah S. Fayaed; Ahmed El-Shafie; Humod Mosad Alsulami; Othman Jaafar; Muhammad Mukhlisin; Amr El-Shafie;

  • 作者单位

    Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment Bangi, Selangor, Malaysia,Ministry of Higher Education, Kingdom of Saudi Arabia, Riyadh 11153, Saudi Arabia;

    Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment Bangi, Selangor, Malaysia;

    Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment Bangi, Selangor, Malaysia,Department of Civil Engineering, Polytechnic Negeri Semarang, Indonesia;

    Faculty of Engineering, University of Benghazi, Benghazi, Libya;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural network; modified stochastic dynamic programing; optimization technique; reservoir operation policy;

    机译：人工神经网络;修改后的随机动态规划;优化技术;水库运行政策;

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