标题：Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems作者： Zou, L (Zou, Ling); Wang, LC (Wang, Lunche); Xia, L (Xia, Li); Lin, AW (Lin, Aiwen); Hu, B (Hu, Bo); Zhu, HJ (Zhu, Hongji)

来源出版物：RENEWABLE ENERGY卷：106 页码：343-353 DOI：10.1016/j.renene.2017.01.042 出版年： JUN 2017

摘要：Solar radiation plays an important role in climate change, energy balance and energy applications. In this work, an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is proposed and compared with Expanded-Improved Bristow-Campbell Model (E-IBCM) and Improved Yang Hybrid Model (IYHM) to predict daily global solar irradiance (H-g) in China. The BCM is expanded by adding meteorological parameters and coefficients calibrated at each station, the YHM is improved by correcting cloud transmittance co-efficients at three stations in Hunan province, China. Daily sunshine duration (S), relative humidity (RH), precipitation (P-re); air pressure (AP), daily mean/maximum/minimum temperature (Delta T/T-max/T-min) are used as inputs for model development and application, while daily H-g is the only output. Performances of different models are evaluated by Root Mean Square Errors (RMSE), Mean Absolute Errors (MAE) and Coefficient of Determination (R-2). The results indicate that the improved empirical models (E-IBCM and IYHM) provides better accuracy than the original models and the ANFIS model is proved to be superior to the E-IBCM and IYHM model in predicting H-g. The statistical results of ANFIS model range 0.59 -1.60 MJ m(-2) day(-1) and 0.42-1.21 MJ m(-2) day(-1) for RMSE and MAE, respectively. The nonlinear modeling process of ANFIS may contribute to its excellent modeling performance.

入藏号：WOS:000395212500034

文献类型：Article

语种：English

作者关键词：Global solar irradiance prediction; Adaptive Neuro-Fuzzy Inference Systems; Bristow-Campbell Model; Yang Hybrid Model; China

扩展关键词：TURBIDITY COEFFICIENT BETA; GLOBAL RADIATION; HYBRID MODEL; MEDITERRANEAN REGION; CHINA; TEMPERATURE; PERFORMANCE; NETWORKS; SURFACES; MACHINE

通讯作者地址： Zou, L; Lin, AW (reprint author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei Province, Peoples R China.

Wang, LC (reprint author), China Univ Geosci, Sch Earth Sci, Lab Crit Zone Evolut, Wuhan 430074, Peoples R China.

电子邮件地址：cheryl_zou@whu.edu.cn; wang@cug.edu.cn; aiwen_lin@l63.com

地址：

[Zou, Ling; Xia, Li; Lin, Aiwen; Zhu, Hongji] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei Province, Peoples R China.

[Wang, Lunche] China Univ Geosci, Sch Earth Sci, Lab Crit Zone Evolut, Wuhan 430074, Peoples R China.

[Wang, Lunche; Hu, Bo] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Phys & Atmospher, Beijing 100029, Peoples R China.

研究方向： Science & Technology - Other Topics; Energy & Fuels

ISSN：0960-1481

影响因子：3.404