TY  - JOUR
A1  - Ahmadi, Mohammad Hossein
A1  - Baghban, Alireza
A1  - Sadeghzadeh, Milad
A1  - Zamen, Mohammad
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
A1  - Kumar, Ravinder
A1  - Mohammadi-Khanaposhtani, Mohammad
T1  - Evaluation of electrical efficiency of photovoltaic thermal solar collector
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - 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.
KW  - Fotovoltaik
KW  - Erneuerbare Energien
KW  - Solar
KW  - Deep learning
KW  - Machine learning
KW  - Renewable energy
KW  - neural networks (NNs)
KW  - adaptive neuro-fuzzy inference system (ANFIS)
KW  - least square support vector machine (LSSVM)
KW  - photovoltaic-thermal (PV/T)
KW  - hybrid machine learning model
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200304-41049
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1734094
VL  - 2020
IS  - volume 14, issue 1
SP  - 545
EP  - 565
PB  - Taylor & Francis
ER  - 
TY  - JOUR
A1  - Mosavi, Amir Hosein
A1  - Qasem, Sultan Noman
A1  - Shokri, Manouchehr
A1  - Band, Shahab S.
A1  - Mohammadzadeh, Ardashir
T1  - Fractional-Order Fuzzy Control Approach for Photovoltaic/Battery Systems under Unknown Dynamics, Variable Irradiation and Temperature
JF  - Electronics
N2  - 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.
KW  - Fuzzy-Logik
KW  - Fotovoltaik
KW  - type-3 fuzzy systems
KW  - fractional-order control
KW  - battery
KW  - photovoltaic
KW  - OA-Publikationsfonds2020
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43381
UR  - https://www.mdpi.com/2079-9292/9/9/1455
VL  - 2020
IS  - Volume 9, issue 9, article 1455
SP  - 1
EP  - 19
PB  - MDPI
CY  - Basel
ER  -