干旱区生态环境知识资源中心(Arid): Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach

Arid

DOI	10.1007/s11053-020-09761-w
	Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach
	Boubaker, Sahbi; Kamel, Souad; Kolsi, Lioua; Kahouli, Omar
通讯作者	Boubaker, S
来源期刊	NATURAL RESOURCES RESEARCH
ISSN	1520-7439
EISSN	1573-8981
英文摘要	Solar photovoltaic (PV) energy is becoming used gradually as an alternative source to classical fossil fuel because of being renewable, clean and abundant. In agricultural regions such as Hail, Saudi Arabia, farmers can invest in using PV sources for pumping water from relatively deep wells. In such case, forecasting the potential of PV energy is among the primary factors that may affect the design of a PV water pumping system efficiency and reliability. In this paper, a novel combined method based on Hammerstein-autoregressive with exogenous input-Wiener model optimized by particle swarm optimization was developed. Four models including, saturation, dead-zone and polynomial nonlinear blocks have been evaluated using global horizontal irradiation (GHI) as model output and separately, the temperature, the clearness index, the relative humidity and the wind speed as model inputs on daily basis collected from Hail region, Saudi Arabia over 20 years (2000-2019). The collected 7305 observations were divided into a training subset (6205) and a testing subset (1095). Six other models including persistence forecast, smart persistence forecast, time series autoregressive and artificial neural networks have been also implemented on the same dataset for comparison purpose. Based on robust performance indicators (mean absolute percentage error, coefficient of determination, the root mean square error and skill score), the Hammerstein-Saturation with temperature as input model outperformed all the other developed models. To validate the suitability of the proposed approach for forecasting GHI in arid climates, our best model was implemented on two locations picked randomly in the Hail region (Saudi Arabia). The results of validation were in the same scale of accuracy as the primary best model.
英文关键词	Global horizontal irradiation (GHI) Forecasting Hammerstein-Wiener model Swarm intelligence Performance indicators
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000578781300001
WOS关键词	SOLAR-RADIATION PREDICTION ; SUPPORT VECTOR MACHINE ; HAMMERSTEIN SYSTEMS ; NEURAL-NETWORK ; TIME-SERIES ; OPTIMIZATION ; IDENTIFICATION ; PERSISTENCE ; SATURATION ; NONLINEARITY
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328360
作者单位	[Boubaker, Sahbi; Kahouli, Omar] Univ Hail, Dept Elect Engn, Community Coll, Hail, Saudi Arabia; [Kolsi, Lioua] Univ Hail, Dept Mech Engn, Coll Engn, Hail, Saudi Arabia; [Boubaker, Sahbi; Kamel, Souad] Univ Jeddah, Dept Comp & Networks Engn, Coll Comp Sci & Engn, Jeddah, Saudi Arabia; [Boubaker, Sahbi] Natl Coll Engn Monastir, Lab Control Elect Syst & Environm LCESE, Monastir, Tunisia
推荐引用方式 GB/T 7714	Boubaker, Sahbi,Kamel, Souad,Kolsi, Lioua,et al. Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach[J].
APA	Boubaker, Sahbi,Kamel, Souad,Kolsi, Lioua,&Kahouli, Omar.
MLA	Boubaker, Sahbi,et al."Forecasting of One-Day-Ahead Global Horizontal Irradiation Using Block-Oriented Models Combined with a Swarm Intelligence Approach".NATURAL RESOURCES RESEARCH