Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield

2013 ◽  
Vol 23 (3) ◽  
pp. 302-318 ◽  
Author(s):  
Mohamed Almaktar ◽  
Hasimah Abdul Rahman ◽  
Mohammad Yusri Hassan ◽  
Ibrahim Saeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tropical Climate ◽  
Photovoltaic System ◽  
Photovoltaic Module ◽  
Energy Yield ◽  
Temperature Estimation ◽  
System Energy ◽  
Artificial Neural

Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods

2021 ◽  
pp. 103149
Author(s):  
Nasim Ghadami ◽  
Mohammad Gheibi ◽  
Zahra Kian ◽  
Mahdieh Ghiyasi Faramarz ◽  
Reza Naghedi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Energy ◽  
Smart Cities ◽  
Photovoltaic System ◽  
Artificial Neural

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

A Study on Artificial Neural Network-based Solar Radiation Forecasting for Efficient Solar Photovoltaic System

2019 ◽  
Vol 29 (6) ◽  
pp. 501-506
Author(s):  
Minseok Kim ◽  
Seunghwan Jung ◽  
Jonggeun Kim ◽  
Hansoo Lee ◽  
Baekcheon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Artificial Neural ◽  
Solar Photovoltaic System
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