scholarly journals Neuro-fuzzy Modelling of a Linear Fresnel-type Solar Collector System as a Digital Twin

2021 ◽  
Author(s):  
William D. Chicaiza ◽  
Adolfo J. Sánchez ◽  
Antonio J. Gallego ◽  
Juan M. Escaño
Keyword(s):  
Solar Collector ◽  
Fuzzy Modelling ◽  
Digital Twin ◽  
Collector System ◽  
Neuro Fuzzy

scholarly journals Machine Learning Prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors

2019 ◽  
Author(s):  
Mohammad Hossein Ahmadi ◽  
Alireza Baghban ◽  
Ely Salwana ◽  
Milad Sadeghzadeh ◽  
Mohammad Zamen ◽  
...  
Keyword(s):  
Solar Collector ◽  
Fossil Fuels ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Support Vector ◽  
Inlet Temperature ◽  
Collector System ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Great Performance

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R2) value of 0.99, respectively.

scholarly journals Evaluation of Electrical Efficiency of Photovoltaic Thermal Solar Collector

2020 ◽  
Author(s):  
Mohammad Hossein Ahmadi ◽  
Alireza Baghban ◽  
Milad Sadeghzadeh ◽  
Mohammad Zamen ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Solar Collector ◽  
Prediction Models ◽  
Renewable Resource ◽  
Support Vector ◽  
Inlet Temperature ◽  
Data Set ◽  
Machine Learning Methods ◽  
Collector System ◽  
Neuro Fuzzy ◽  
Vector Machines

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. 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 inputs 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 approaches and evaluate their performance. The proposed LSSVM model outperformed 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.

Solar Water Heater Design for Houses in Arid Zones

2013 ◽  
Author(s):  
Ramses Vega ◽  
Hector E. Campbell ◽  
Juan de Dios Ocampo ◽  
Diego R. Bonilla G.
Keyword(s):  
Solar Collector ◽  
Extreme Temperature ◽  
Hot Water ◽  
Arid Zones ◽  
Water Heater ◽  
Collector System ◽  
End Use ◽  
Flat Plate Solar Collector ◽  
Demand Profile ◽  
Heater Design

This paper shows the simulation and design of a flat plate solar collector system, used to feed hot water to a typical home located in the city of Mexicali, Baja California, México. The system consists of a solar collector, a storage tank, a water pump and accessories and special tools that allow its proper operation. Analyzing the consumption and end use of water in a typical House, a demand profile is established, which combined with the weather information of the region, constitutes the input parameters required for the simulation of the system, which is performed with the software package TRNSYS. Mexicali, due to its location (latitude 32 °, longitude 114 °) and semi-desert condition presents high temperatures in the summer and low in winter, so the design and operation of such systems require special features, not always considered in the conventional ratings. This paper presents methods for simulation and design oriented to optimize the dimensioning and operation of this type of solar heaters in regions with extreme temperature conditions.

scholarly journals Coactive neuro-fuzzy modelling for colour recipe prediction

2002 ◽  
Author(s):  
E. Mizutani ◽  
J.-S.R. Jang ◽  
K. Nishio ◽  
H. Takagi ◽  
D.M. Anslander
Keyword(s):  
Fuzzy Modelling ◽  
Neuro Fuzzy

Flat-Plate Solar Collector in Transient Operation: Modeling and Measurements

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Ahmad M. Saleh ◽  
Donald W. Mueller ◽  
Hosni I. Abu-Mulaweh
Keyword(s):  
Differential Equations ◽  
Flat Plate ◽  
Solar Collector ◽  
Storage Tank ◽  
Ambient Air ◽  
Fluid Temperature ◽  
Ambient Conditions ◽  
Flow Rates ◽  
Collector System ◽  
Nodal Model

This paper describes a mathematical model for simulating the transient processes which occur in liquid flat-plate solar collectors. A discrete nodal model that represents the flat-plate solar collector's layers and the storage tank is employed. The model is based on solving a system of coupled differential equations which describe the energy conservation for the glass cover, air gap, absorber, fluid, insulation, and the storage tank. Inputs to the model include the time-varying liquid flow rate, incident solar radiation, and the ambient air temperature, as well as the volume of liquid in the storage tank and initial temperature of the system. The system of differential equations is solved iteratively using an implicit, finite-difference formulation executed with Matlab software. In order to verify the proposed method, an experiment was designed and conducted on different days with variable ambient conditions and flow rates. The comparison between the computed and measured results of the transient fluid temperature at the collector outlet shows good agreement. The proposed method is extremely general and flexible accounting for variable ambient conditions and flow rates and allowing for a geometrical and thermophysical description of all major components of the solar collector system, including the storage tank. The validated, general model is suitable to investigate the effectiveness of various components without the necessity of carrying out experimental work, and the flexible computational scheme is useful for transient simulations of energy systems.

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