兰州大学机构库 >数学与统计学院
基于相空间重构理论和误差矫正模型的组合风速预测模型的研究与应用
Alternative TitleThe investigation and application of hybrid wind speed forecasting model based on phase space reconstruction theory and error correction model
汪运
Thesis Advisor王建州
2015-05-24
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword相空间重构 最小二乘支持向量机 模糊C均值聚类 Markov模型 误差矫正
Abstract近年来,新能源的开发与利用已成为一个热点问题,其中风能以其巨大的潜力被广泛应用,而风能利用的最重要的形式就是风力发电。但由于自然界的风具备较强的随机性及间歇性,使得风功率具有较强的波动性及不可控性。针对我国大规模风电接入电网的现实以及风能资源的间歇性特点,如果可以使风速更准确的预测,将有利于电网调度部门及时制定调度计划,缓解风电的间歇性对电网造成的不利影响,确保电网的安全和稳定。为了更加准确的进行风速预测研究,本文提出一种基于最小二乘支持向量机和误差矫正模型的组合预测算法。受到很多因素如气温,气压和湿度等影响,风速的波动可以看成是一个复杂的非线性动力系统。因此本文将相空间理论运用于风速预测当中,并用C-C方法进行风速预测的输入集决定。然后在输入级决定后运用LSSVM模型对风速进行预测,与此同时,LSSVM模型当中的参数在这里我们运用粒子群优化算法结合重力搜索的组合优化算法决定(PSOGSA)。该组合优化算法的优点在于它的快速收敛性能。此外,由于风速波动的复杂性,上述模型可能无法挖掘出风速波动的全部特性,残差序列可能包含有被上述模型忽略的反映风速波动的重要信息。因此,运用Markov模型和模糊C均值模型构建误差矫正模型,以期望通过误差矫正能得到更好的预测结果。仿真结果显示,就预测精度而言,该模型比本文中讨论的其他模型有更好的预测准确性。
Other AbstractRecently, the development and utilization of renewable energy have become a hot issue, in which wind energy is widely used for its great potential, and the most important form of wind energy is wind power. But due to the randomness and intermittent traits of wind, wind power has strong volatility and uncontrollability. Regarding the reality that the large-scale wind power is connected into power grid as well as the intermittent characteristics of wind power, if more accurate wind speed forecasts is obtained, that will be conducive to develop a scheduling plan by dispatching department timely, alleviate the adverse impact caused by intermittency characteristic of wind power, and ensure the security and stability of the power grid. With the aim of developing accurate tools for forecasting wind speed, this paper presents a novel hybrid intelligent forecasting model based on Least Square Support Vector Machine and the Markov model. However, the fluctuation of wind speed affected by many factors such as temperature, pressure, and humidity is a complex nonlinear dynamic system. So we group the space theory into the wind speed forecasting in this paper, and employ C-C method to determine the input form. Subsequently, the LSSVM model, which is optimized using the hybrid optimization algorithm PSOGSA, is employed to forecast the nonlinear parts of the wind speed for its faster convergence property. Additionally, because of the complex fluctuation of wind speed, the upper forecasting model may dig limited characteristics of wind speed fluctuations, and leave some important information that reflect the fluctuation of wind speed in residual series. Therefore, we construct an error correction model that is based on Markov model and FCM model to make adjustments with the hope of achieving better forecasting results. The simulation results indicate that the proposed model can outperform the other models discussed in this paper with respect to forecasting accuracy.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225099
Collection数学与统计学院
Recommended Citation
GB/T 7714
汪运. 基于相空间重构理论和误差矫正模型的组合风速预测模型的研究与应用[D]. 兰州. 兰州大学,2015.
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