基于在线高斯过程回归的短期风电功率概率预测  

Short-term Wind Power Probability Prediction Based on Online Gaussian Process Regression

作　　者：王耀健 顾洁[1] 温洪林 金之俭[1] WANG Yaojian;GU Jie;WEN Honglin;JIN Zhijian(Key Laboratory of Control of Power Transmission and Transformation of Ministry of Education(Shanghai Jiao Tong University),Shanghai 200240,China)

机构地区：[1]电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市200240

出　　处：《电力系统自动化》2024年第11期197-207,共11页Automation of Electric Power Systems

基　　金：上海市科委科研计划资助项目(18DZ1100303)。

摘　　要：预测未来几个小时的风电出力对于电力系统的安全经济运行以及风电参与电力市场至关重要。由于风电功率序列具有强随机性与非平稳性,需要对其不确定性进行刻画建模。同时,考虑到机组设备老化、叶片污染及风电场环境变化等因素会影响风电机组的出力特性,从而使得风电功率预测模型参数存在时变,增大了风电功率预测难度。文中提出了一种基于在线高斯过程的短期风电概率预测方法:首先,利用高斯过程回归模型对风电功率预测问题进行建模;然后,结合结构化内核插值与Woodbury恒等式,降低高斯过程计算复杂度,实现高斯过程的快速求解;最后,采用分块缓存与更新的方法实现高斯过程模型参数与超参数实时在线更新。经2014年全球能源预测大赛发布的风力发电数据验证,结果表明所提算法具有较好的预测性能,同时具有自适应性,可以有效应对模型参数时变问题。Predicting the wind power output in the next few hours is crucial for the safe and economic operation of the power system as well as the participation of wind power in the electricity market.Due to the strong randomness and non-stationarity of the wind power sequence,it is necessary to characterize and model its uncertainty.Moreover,factors such as equipment aging,blade contamination,and changes in the wind farm environment can affect the output characteristics of wind turbines,thereby making the parameters of the wind power prediction model time-varying and increasing the difficulty of wind power prediction.This paper proposes a short-term wind power probability prediction method based on online Gaussian process.First,the Gaussian process regression model is used to model the wind power prediction problem.Then,the complexity of Gaussian process calculation is reduced by combining structured kernel interpolation with the Woodbury identity,which enables fast Gaussian process solving.Finally,the method of block caching and updating is adopted to realize the real-time online updating of parameters and hyperparameters for Gaussian process model.The wind power generation data released by the 2014 Global Energy Forecasting Competition is used to validate the proposed algorithm.The results show that the proposed algorithm has good prediction performance and adaptability to effectively deal with the problem of time-varying model parameters.

关 键 词：风电功率预测 概率预测 时变参数 高斯过程 在线学习 不确定性估计 

分 类 号：G63[文化科学—教育学]