电力系统保护与控制2017，Vol.45Issue(5)：73-79,7.DOI:10.7667/PSPC160389
基于相空间重构和高斯过程回归的短期负荷预测
Short-term load forecasting based on phase space reconstruction and Gaussian process regression
顾熹 1廖志伟1
作者信息
- 1. 华南理工大学电力学院,广州广东510640
- 折叠
摘要
Abstract
According to the chaotic features of load series,a new forecasting method combining phase space reconstruction and Gaussian process regression is proposed.Firstly,two parameters of time series (delay time and delay window) are earned at the same time by means of the C-C method.Secondly,the reconstructed series of the separate load as well as the multi-variable model considering load and other influence factors are established.Then,the load sample is trained by GPR models using both single and composite kernel function and the optimal hyper-parameters are calculated,with which the 24-hour daily loads are predicted.Finally,the forecasting consequence of the single load model is contrasted with SVM model and the multi-variable GP model.Prediction results indicate that the model using multi-variable and composite kernel function achieves better effects and the new method is not only feasible but also satisfies the requirements of the engineering precision.关键词
相空间重构/高斯过程回归/C-C方法/短期负荷预测/组合核函数Key words
phase space reconstruction/Gaussian process regression/C-C method/short-term load forecasting/composite kernel function引用本文复制引用
顾熹,廖志伟..基于相空间重构和高斯过程回归的短期负荷预测[J].电力系统保护与控制,2017,45(5):73-79,7.
