DOI: 10.1016/j.seta.2019.100574
论文题名: Machine learning-based improvement of empiric models for an accurate estimating process of global solar radiation
作者: Demircan C. ; Bayrakçı H.C. ; Keçebaş A.
刊名: Sustainable Energy Technologies and Assessments
ISSN: 22131388
出版年: 2020
卷: 37 语种: 英语
英文关键词: Artificial bee colony
; Empirical models
; Global solar radiation
; Solar energy
; Sunshine duration
Scopus关键词: Machine learning
; Optimization
; Solar energy
; Artificial bee colonies
; Artificial bee colony algorithms (ABC)
; Empirical model
; Global solar radiation
; Multiple-modeling
; Statistical errors
; Sunshine duration
; System planning
; Solar radiation
; algorithm
; empirical analysis
; machine learning
; numerical model
; solar power
; solar radiation
; Mugla
; Turkey
; Apoidea
英文摘要: The change of the solar radiation reaching the earth depending on specific conditions brings the execution of system planning meticulously and optimally by solar power researchers to the fore. For the estimation of the solar radiation, the most frequently used model is the Angtröm-Prescott model. In this model, sunshine ratio plays an important role. In the study, it is attempted to enhance the annual and semi-annual models developed for the city of Muğla, Turkey and to congregate the semi-annual models in a single model by using the Artificial Bee Colony (ABC) algorithm. The results obtained have revealed that in the multiple model relying on only the sunshine duration, the statistical error values were not reduced to very low levels. In order to cope with this problem, the multiple model relying on both the sunshine duration and the sunset-sunrise hour angle has been proposed. In this way, the statistical errors are found to be reduced by about 40% using the ABC algorithm and the multiple model. It was seen that the models recommended are superior to all the models especially in summer and spring months when there is plenty of sunshine. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159590
Appears in Collections: 气候变化与战略
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作者单位: Department of Energy Systems Engineering, Graduate School of Natural and Applied Sciences, Süleyman Demirel University, Isparta, 32260, Turkey; Department of Mechatronics Engineering, Faculty of Technology, Isparta University of Applied Sciences, Isparta, 32260, Turkey; Department of Energy Systems Engineering, Faculty of Technology, Muğla Sıtkı Koçman University, Muğla, 48000, Turkey
Recommended Citation:
Demircan C.,Bayrakçı H.C.,Keçebaş A.. Machine learning-based improvement of empiric models for an accurate estimating process of global solar radiation[J]. Sustainable Energy Technologies and Assessments,2020-01-01,37