An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they we...

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Bibliographic Details
Published in:Energies (Basel) 2019, Vol.12 (14), p.2662
Main Authors: Pitalúa-Díaz, Nun, Arellano-Valmaña, Fernando, Ruz-Hernandez, Jose A, Matsumoto, Yasuhiro, Hussain Alazki, Herrera-López, Enrique J, Hinojosa-Palafox, Jesús Fernando, García-Juárez, A, Pérez-Enciso, Ricardo Arturo, Velázquez-Contreras, Enrique Fernando
Format: Article
Language:English
Subjects:
Adaptive systems
ANFIS
Brain
Electric power
Errors
Fuzzy logic
Fuzzy sets
Fuzzy systems
gradient descent
Intelligent systems
Learning algorithms
Neural networks
Optimization
Photovoltaic cells
photovoltaic system
Photovoltaics
Regression analysis
Solar energy
Solar radiation
Statistical analysis
statistical method
Statistical methods
Stock exchanges
Studies
sustainable development
Variables
Wind speed
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Summary:In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12142662