Optimal Granule-Based PIs Construction for Solar Irradiance Forecast

被引：25

作者：

Chai, Songjian ^{[1
]}

Xu, Zhao ^{[1
]}

Wong, Wai Kin ^{[2
]}

机构：

[1] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China

[2] Hong Kong Observ, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2016年 / 31卷 / 04期

关键词：

Granular neural network; prediction intervals; random vector forward link (RVFL); solar irradiance forecasting;

D O I：

10.1109/TPWRS.2015.2473097

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 letter proposes a novel granule computing-based framework for prediction intervals (PIs) construction of solar irradiance time series that has significant impacts on solar power production. Distinguished from most existing methods, the new framework can address both stochastic and knowledge uncertainties in constructing PIs. The proposed method has proved to be highly effective in terms of both reliability and sharpness through a real case study using measurement data obtained from Hong Kong Observatory.

引用

页码：3332 / 3333

页数：2

共 3 条

[1] Genetic interval neural networks for granular data regression [J].

Cimino, Mario G. C. A. ;

Lazzerini, Beatrice ;

Marcelloni, Francesco ;

Pedrycz, Witold .

INFORMATION SCIENCES, 2014, 257 :313-330

[2] Granular Neural Networks: Concepts and Development Schemes [J].

Song, Mingli ;

Pedrycz, Witold .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2013, 24 (04) :542-553

[3] Optimal Prediction Intervals of Wind Power Generation [J].

Wan, Can ;

Xu, Zhao ;

Pinson, Pierre ;

Dong, Zhao Yang ;

Wong, Kit Po .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2014, 29 (03) :1166-1174

← 1 →