Prediction of daily diffuse solar radiation using artificial neural networks

被引：70

作者：

Xue, Xinhua ^{[1
]}

机构：

[1] Sichuan Univ, Coll Water Resource & Hydropower, State Key Lab Hydraul & Mt River Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Sichuan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY | 2017年 / 42卷 / 47期

关键词：

Diffuse solar radiation; Back propagation neural network; Genetic algorithm; Particle swarm optimization; EXTREME LEARNING-MACHINE; HYDROGEN-PRODUCTION; HORIZONTAL SURFACE; EMPIRICAL-MODELS; FUZZY APPROACH; ENERGY; ALGORITHM; FRACTION;

D O I：

10.1016/j.ijhydene.2017.09.150

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

This study presents two optimization techniques, genetic algorithm (GA) and particle swarm optimization (PSO), to improve the efficiency and generalization ability of back propagation neural network (BPNN) model for predicting daily diffuse solar radiation. Seven parameters including month of the year, sunshine duration, mean temperature, rainfall, wind speed, relative humidity, and daily global solar radiation are selected as the evaluating indices. The predictions from the BPNN optimized by PSO model were compared with those from two models: BPNN and BPNN optimized by GA. The results show that the proposed BPNN optimized by PSO model has potential in accurately predicting the daily diffuse solar radiation. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

引用

页码：28214 / 28221

页数：8

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