Echo state network-based spatio-temporal model for solar irradiance estimation

被引：5

作者：

Li, Qian ^{[1
,2
]}

Wu, Zhou ^{[1
,2
]}

Ling, Rui ^{[2
]}

Tan, Mi ^{[2
]}

机构：

[1] Minist Educ, Key Lab Complex Syst Safety & Control, Beijing, Peoples R China

[2] Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China

来源：

INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS | 2019年 / 158卷

基金：

中国国家自然科学基金;

关键词：

Echo state network; Spatio-temporal; Solar irradiation forecast; NEURAL-NETWORK; PREDICTION; POWER;

D O I：

10.1016/j.egypro.2019.01.868

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this study, echo state network-based spatio-temporal (STESN) model is proposed to predict the solar irradiance of a given target station, depending on the historical data of its surrounding sites. Hourly solar irradiance datasets for the whole year 2017, obtained from California Irrigation Management Information System (CIMIS) at eight different stations are used. Comparison studies show that SPESN could achieve more accurate prediction, compared with the conventional persistence (PSS) benchmark model. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：3808 / 3813

页数：6

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