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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/25071
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dc.contributor.authorKumari P.-
dc.contributor.authorToshniwal, Durga-
dc.date.accessioned2022-04-22T05:54:06Z-
dc.date.available2022-04-22T05:54:06Z-
dc.date.issued2021-
dc.identifier.citationInternational Journal of Green Energy, 18(10): 1007-1026-
dc.identifier.issn15435075-
dc.identifier.urihttps://doi.org/10.1080/15435075.2021.1890085-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/25071-
dc.description.abstractSolar resource availability of a location depends on the local meteorological parameters. Therefore, the selection of suitable meteorological variable is highly desirable to develop the accurate solar irradiance prediction models. In present study, global horizontal irradiance (GHI) prediction models are developed using artificial neural network (ANN) with different combinations of meteorological parameters. A four-year station measured dataset consists of minimum temperature ((Formula presented.)), maximum temperature ((Formula presented.)), temperature difference ((Formula presented.)), GHI, extraterrestrial radiation ((Formula presented.)), and bright sunshine hours ((Formula presented.)) have been employed to establish ANN models. Five types of ANN models (ANN-1 to ANN-5) are developed with 32 possible input combinations to determine the best input combinations to predict the daily GHI accurately. The achieved maximum correlation coefficient for ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5 models are 0.9197, 0.9681, 0.9688, 0.9515, and 0.9457, respectively. The results revealed that ANN-2 and ANN-3 has shown best performance with the input combinations of [(Formula presented.)] and (Formula presented.) respectively. The proposed methodology is also used to assess the solar potential of the mountainous state of Uttarakhand, India, situated in foots of Himalayas, using the best ANN model. The obtained results suggest that Uttarakhand has good solar potential with annual GHI varies from 16.96 to 19.54 (Formula presented.), which is sufficient to implement a broad range of solar applications in the region. The methodology proposed in this work can be utilized to develop solar irradiance prediction models for different locations where monitoring stations are not available. © 2021 Taylor & Francis Group, LLC.-
dc.language.isoen_US-
dc.publisherBellwether Publishing, Ltd.-
dc.relation.ispartofInternational Journal of Green Energy-
dc.subjectartificial neural network-
dc.subjectglobal horizontal irradiance-
dc.subjecthimalayas-
dc.subjectSolar potential-
dc.subjectsunshine hour-
dc.subjecttemperature-
dc.titleAnalysis of ANN-based daily global horizontal irradiance prediction models with different meteorological parameters: a case study of mountainous region of India-
dc.typeArticle-
dc.scopusid57205766083-
dc.scopusid8683737500-
dc.affiliationKumari, P., Department of Computer Science Engineering, Indian Institute of Technology Roorkee, Roorkee, India-
dc.affiliationToshniwal, D., Department of Computer Science Engineering, Indian Institute of Technology Roorkee, Roorkee, India-
dc.description.funding-
dc.description.correspondingauthorKumari, P.; Department of Computer Science Engineering, India; email: pratimatomar93@gmail.com-
Appears in Collections:Journal Publications [CS]

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