| 基于神经网络集成算子的混合模型的研究与应用 |
| Alternative Title | Research and Application on Hybrid Models Based on the Ensemble Operators of Neural Networks
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| 张艺馨 |
| Thesis Advisor | 王建州
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| 2015-05-24
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| Degree Grantor | 兰州大学
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| Place of Conferral | 兰州
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| Degree Name | 硕士
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| Keyword | 时间序列预测
神经网络
集成算子
优化算法
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| Abstract | 随着世界上风力发电的快速发展，风电在电网中所占的比例不断增大。然而，准确的风速预测仍然是目前大规模的风力发电的一个挑战。因此，准确地预测风风速对于风力发电、实现电力系统的优化运行和调度，以及减少电力系统的运营成本是非常重要的。
自回归求和移动平均时间序列模型（ARIMA模型）可以通过对原序列的差分得到平稳的时间序列，广义自回归条件异方差模型（GARCH模型）在ARIMA模型的基础上，考虑了预测残差的条件异方差，增加了条件方差方程，而滚动灰色模型（RGM模型）是在灰色建模的基础上，通过循环迭代实现数据更新。
人工智能神经网络方法由于其良好的非线性拟合能力和泛化能力，用以代替传统的时间序列预测方法。其中，BP神经网络是一种前馈型的神经网络，可以有效的根据误差不断的调节网络结构参数，从而达到理想的预测效果；而小波神经网络则是在BP神经网络的基础上，结合小波分析的优点，引起了研究者极大的重视。
与包括ARIMA模型、GARCH模型和RGM模型在内的三种传统时间序列模型以及单一的BP神经网络和小波神经网络相比，本文以神经网络为基础，针对其结构初始化的随机性问题，结合集成预测的思想，给出了集成BP神经网络和集成小波模型风速预测模型，可以避免因为不确定的权重导致的缺陷。在构造的平均数集成算子、中位数集成算子和众数集成算子三种集成算子下，集成网络的预测输出与单一的神经网络输出相比较有一定的改善。同时为了进一步提高预测的准确性，本文分别利用粒子群优化（PSO）算法和布谷鸟搜索（CS）算法对计算众数算子网络输出的核密度估计中的窗宽参数进行优化。在内蒙古的风速预测研究中表明，本文提出的基于神经网络集成算子的混合预测模型在对中国内蒙古中部风速的模拟预测中优于其他五种传统单一的预测模型。 |
| Other Abstract | With the rapid development of wind power in the world, the proportion of wind power in Power grid is increasingly larger.Therefore, accurate forecasting of the output of the wind power is necessary so as to achieve the optimal operation and dispatching of the Power system, as well as reducing the power system spinning reserve and operating costs. The Autoregressive Integrated Moving Average Model, ARIMA model, can obtain a stationary series by conducting difference on the original data. Moreover, on the basis of the ARIMA model, Generalized Autoregressive Conditional Heteroskedasticity Model, GARCH model, takes the conditional heteroskedasticity into consideration and adds a conditional heteroskedasticity equation. Based on grey model, Rolling Grey Model, RGM, continually adopts new data while eliminate old data to fulfill data update. Artificial Neural Networks, ANN, are adopted to replace the traditional time series methods for its good nonlinear fitting capacity and generalization ability. With the back propagation of its output error, Back Propagation neural network can adjust its weights and thresholds between each layers to get a better performance.In this paper, compared to the traditional time series model, ARIMA, GARCH and RGM included, and the single BPNN and WNN, the hybrid models based on the ensemble operators of neural networks is proposed on the basis of single BP network and single wavelet network and ensemble forecasting method, which avoids the flaws because of the uncertainty weights and local minimum of neural networks by adopting the ensemble operators. Under the conduction of three ensemble operators, Mean, Median and Mode, the performance of the ensemble models have been improved to some extent compared to single neural networks.The experimental study of the wind speed in Inner Mongolia of China reveals that the results of the proposed hybrid model is much more better than the other five traditional single forecasting models. |
| URL | 查看原文
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| Language | 中文
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| Document Type | 学位论文
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| Identifier | https://ir.lzu.edu.cn/handle/262010/225127
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| Collection | 数学与统计学院
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Recommended Citation
GB/T 7714 |
张艺馨. 基于神经网络集成算子的混合模型的研究与应用[D]. 兰州. 兰州大学,2015.
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