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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.…
Document Type: | Article |
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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 and partner institutions: | Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM) |
Volume: | 2020 |
Issue: | volume 14, issue 1 |
Pagenumber: | 22 |
First Page: | 545 |
Last Page: | 565 |
Tag: | OA-Publikationsfonds2020 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): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |