@article{AhmadiBaghbanSadeghzadehetal.,
  author    = {Ahmadi, Mohammad Hossein and Baghban, Alireza and Sadeghzadeh, Milad and Zamen, Mohammad and Mosavi, Amir and Shamshirband, Shahaboddin and Kumar, Ravinder and Mohammadi-Khanaposhtani, Mohammad},
  title     = {Evaluation of electrical efficiency of photovoltaic thermal solar collector},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2020},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {volume 14, issue 1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2020.1734094},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200304-41049},
  pages     = {545 -- 565},
  abstract  = {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.},
  subject      = {Fotovoltaik},
  language  = {en}
}
@article{MosaviQasemShokrietal.,
  author    = {Mosavi, Amir Hosein and Qasem, Sultan Noman and Shokri, Manouchehr and Band, Shahab S. and Mohammadzadeh, Ardashir},
  title     = {Fractional-Order Fuzzy Control Approach for Photovoltaic/Battery Systems under Unknown Dynamics, Variable Irradiation and Temperature},
  series = {Electronics},
  volume    = {2020},
  journal   = {Electronics},
  number    = {Volume 9, issue 9, article 1455},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/electronics9091455},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210122-43381},
  pages     = {1 -- 19},
  abstract  = {For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated.},
  subject      = {Fuzzy-Logik},
  language  = {en}
}