Gaussian Process prior models for electrical load forecasting

被引：0

作者：

Leith, DJ

Heidl, M

Ringwood, JV

机构：

来源：

2004 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS | 2004年

关键词：

electricity demand; Gaussian processes; load forecasting; modeling;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper examines models based on Gaussian Process (GP) priors for electrical load forecasting. This methodology is seen to encompass a number of popular forecasting methods, such as Basic Structural Models (BSMs) and Seasonal Auto-Regressive Intergrated (SARI) as special cases. The GP forecasting models are shown to have some desirable properties and their performance is examined on weekly and yearly Irish load data.

引用

页码：112 / 117

页数：6

共 27 条

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