Evaluation of electrical efficiency of photovoltaic thermal solar collector

  • In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables.In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.show moreshow less

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    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Mohammad Hossein Ahmadi, Alireza BaghbanORCiD, Milad SadeghzadehORCiD, Mohammad ZamenORCiD, Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD, Ravinder Kumar, Mohammad Mohammadi-KhanaposhtaniORCiD
DOI (Cite-Link):https://doi.org/10.1080/19942060.2020.1734094Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200304-41049Cite-Link
URL:https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094
Parent Title (English):Engineering Applications of Computational Fluid Mechanics
Publisher:Taylor & Francis
Language:English
Date of Publication (online):2020/02/26
Date of first Publication:2020/02/26
Release Date:2020/03/04
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 14, issue 1
Pagenumber:22
First Page:545
Last Page:565
Tag:Deep learning; Machine learning; Renewable energy; Solar; adaptive neuro-fuzzy inference system (ANFIS); hybrid machine learning model; least square support vector machine (LSSVM); neural networks (NNs); photovoltaic-thermal (PV/T)
GND Keyword:Fotovoltaik; Erneuerbare Energien
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)