一种基于改进GPR和Bagging的短期风电功率组合预测方法

摘要

In order to improve the accuracy of short-term wind power forecasting and quantify the uncertainty of power prediction, a combination forecasting method based on Gaussian Process Regression (GPR) and Bootstrap Aggregation (Bagging) is proposed. For the instability and large computing of GPR, Bagging and Fully Independent Training Conditional Approximation (FITC) are introduced. Meanwhile, a new weight strategy based on Bayesian Committee Machine (BCM) is raised. Experiments show that, GPR method which based on Bagging and FITC is better than the traditional method in the stability, precision and training time consuming. Furthermore the improved GPR can get a more accurate confidence interval, and the prediction accuracy of the proposed method also has improved significantly compared with ELM and LSSVM in the wind power prediction.%为提高短期风电功率的预测精度并对功率预测的不确定性进行量化，提出了基于高斯过程回归(Gaussian Process Regression，GPR)和Bootstrap Aggregation (Bagging)的组合预测方法。针对GPR的不稳定性和计算量大的特点，引入了Bagging和训练数据完全条件独立下的近似方法(Fully Independent Training Conditional Approximation，FITC)。同时，在贝叶斯决策(Bayesian Committee Machine, BCM)的基础上，提出了一种新的权重组合策略。实验表明，基于Bagging和FITC的GPR方法在稳定性、预测精度和训练时间的消耗上都优于传统的GPR方法。在风电功率预测中，改进的GPR可以给出较准确的置信区间，且与极限学习机、最小二乘支持向量机相比较，该方法的预测精度也有明显提高。