基于高斯过程的短期风电功率概率预测方法研究

项目名称： 基于高斯过程的短期风电功率概率预测方法研究

项目编号： No.51467008

项目类型： 地区科学基金项目

立项/批准年度： 2015

项目学科： 电工技术

项目作者： 李军

作者单位： 兰州交通大学

项目金额： 45万元

中文摘要： 风电功率预测是应对风电大规模并网运行问题的重要手段，对智能电网的发展规划和调度具有重要意义。概率预测有别于期望值预测，能够提供被预测量的分布信息，并对模型输出的不确定性提供度量。鉴于高斯过程(GP)在时间序列预测上的成功应用，结合数值天气预报信息，在解决输入特征信息提取等关键问题基础上，统一在非线性时间序列分析的框架下，研究基于GP的短期风电功率概率预测方法，以建立适应性更强的概率预测模型。具体包括：为解决大数据集的离线训练和在线学习，提出研究基于不同稀疏逼近算法的GP模型；基于非线性状态空间推理和学习的在线GP模型；延伸至非高斯似然性的鲁棒稀疏GP模型。为进一步提高预测模型的准确度和泛化能力，结合局部学习与集成学习策略，研究基于集成经验模态分解与局部GP结合的组合预测以及基于局部多模型GP集成的组合预测方法。本项目从国家建设与甘肃地区风电发展需求出发，具有重要的理论与工程应用价值。

中文关键词： 风电功率；概率预测；高斯过程；时间序列；特征提取

英文摘要： Wind power forecasting is an important means to cope with problems when large scale wind power is integrated into grid, which is of great significance for development programs and dispatching in smart grid.Probabilistic forecast is different expection forecast by the capability of forecasting the distribution of random variables, in addition it also provides a measure of the model uncertainty for wind power generation. In view of successful application in time series forecasting by using Gaussian processes(GP) methods, under the framework of time series analysis with strong nonlinear characteristics, combinated the NWP information with wind power data, on the basis of select the key input varable by feature extraction methods, the project will study short term wind power probabilistic forecasting methods for wind farm using Gaussian process regression techniques to build enhanced and more higher versatility wind power forecasting model.This main contents of the project are as follows: to attempt to solve some main problems of modelling large data sets as well as online learning for wind power probabilistic forecasting,studying several sparse Gaussian processes models with different sparse approximation algorithms, several effective sparse online Gaussian process models with different sparse online approximation algorithms,the extension of sparse robust Gaussian process models to non-Gaussian likelihoods, the extension of nonlinear state space model with online Gaussian processes method so that the super-short term and short term wind power probabilistic forecasting will be well performed. Moreover,to further improve the prediction accuracy and generalization of the forcasting model, combination forecasting approaches based on online local gaussian processes model using ensembling multi-model learning strategies are proposed, simutianeously, a kind of combined forecasting method based on complete ensemble empirical model decomposition and local Gaussian process model is also studied. According to the imperious demands of the national development strategy as well as the construction of wind power projects in Gansu region，this project possesses a significant value in the theory and engineering research for short-term wind power probabilistic forecasting.

英文关键词： wind power;probabilistic forecasting;gaussian processes;time series;feature extraction