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

Pitalúa-Díaz, Nun; Arellano-Valmaña, Fernando; Ruz-Hernández, José A.; Matsumoto, Yasuhiro; Alazki, Hussain; Herrera-López, Enrique J.; Hinojosa-Palafox, J.F.; García-Juárez, A.; Pérez-Enciso, Ricardo Arturo; Velázquez-Contreras, Enrique F.

doi:10.3390/en12142662

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…El análisis del error fue realizado aplicando el MAE (Mean Absolute Error o Error Medio Absoluto) y el MAPE (Mean Absolute Percentage Error o Error Porcentual Medio Absoluto) como técnicas de evaluación de precisión por ser de los más utilizados en sistemas inteligentes. MAE y MAPE están descritas por las ecuaciones 8 y 9, respectivamente, donde N es la cantidad total de muestras, s es la muestra a considerar, Pm es el valor de potencia real o medida y Pe es la potencia estimada por el ANFIS (Dev et al, 2018;Pitalúa-Díaz et al, 2019;Ruz-Hernandez et al, 2019 A partir de la Figura 6 y Figura claramente se logra apreciar un comportamiento lineal de los datos puntuales (verde) el cual se comprueba con la línea de tendencia (roja) indicando que la estimación del modelo implementado es lineal y directamente proporcional a los valores reales. A su vez, la Tabla 2 detalla los valores obtenidos por cada método evaluado.…”

Section: Resultados Y Discusiónunclassified

“…Para superar este problema, muchos investigadores han optado por emplear métodos inteligentes (Grimaccia et al, 2011;Kashyap et al, 2015) debido a su capacidad de representar procesos lineales y no lineales con precisión, así como de estimar un sistema del cual no se conoce claramente su dinámica interna obteniendo mejores resultados que por métodos puramente estadísticos (Pitalúa-Díaz et al, 2019;Ruz-Hernandez et al, 2019).…”

Section: Introductionunclassified

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Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico

Arellano-Valmaña

2019

JEE

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Solar energy is currently the most resourceful renewable source worldwide in which its obtainment, unlike many others currently used, does not mean any harm to the environment. To manage satisfactorily the electricity demand of photovoltaic systems a careful planning is required, nonetheless, to set successfully the solar behavior and even more to predict the amount of incident solar radiation is a completely stochastic process due the influence of meteorological variables. To overcome this problem, this study monitors diverse atmospheric variables by using a meteorological station located in the University of Sonora in Hermosillo city, Mexico; and registers real-time measure values every 5 minutes for 6 months. From the data obtained a computational model applying an Adaptive Neuro Fuzzy Inference System (ANFIS) as intelligent technique is presented in order to forecast the electrical power from a photovoltaic system installed in the same site. It is vital to understand the influence of meteorological variables on energy consumption in which a better understanding of it can contribute to a more useful strategy in meeting energy efficiency goal for Mexico.

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Section: Resultados Y Discusiónunclassified

Section: Introductionunclassified

Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico

Arellano-Valmaña

2019

JEE

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“…It has been concluded from this research that the ANFIS-based model overcomes both multiple linear regression and gradient descent optimization methods. 18 For real-time applications, a comprehensive survey has been carried out based on neuro-fuzzy rule generation algorithm for delivering maximum power to the load based on the maximum power point (MPP) as it gives a faster response with precision and accuracy. Many grid-connected solar PV plants have been based on PV technology, but variation in weather condition makes the system output nondeterministic and stochastic.…”

Section: Introductionmentioning

confidence: 99%

“…In one of the recent research, Diaz et al proposed an ANFIS‐based model for obtaining the best electrical power estimation from the solar PV system. It has been concluded from this research that the ANFIS‐based model overcomes both multiple linear regression and gradient descent optimization methods . For real‐time applications, a comprehensive survey has been carried out based on neuro‐fuzzy rule generation algorithm for delivering maximum power to the load based on the maximum power point (MPP) as it gives a faster response with precision and accuracy.…”

Section: Introductionmentioning

confidence: 99%

An ANFIS‐based model for solar energy forecasting and its smart grid application

Perveen

Rizwan

Goel

2019

Engineering Reports

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In the present work, a model underlying the principle of adaptive neural‐fuzzy inference system (ANFIS) architecture is proposed. This research investigates the ANFIS‐based model performance to forecast global solar energy under different weather conditions, namely, clear sky, hazy sky, hazy and cloudy sky, and cloudy sky; and for distinct climate zones using salient meteorological parameters. Power generation from solar photovoltaic (PV) may be affected by different factors, such as cloud cover, topographical locations, time, and seasonal variations. Therefore, accurate forecasting of PV power is an important factor for system reliability and robustness. In this work, the proposed model is implemented for smart grid applications in forecasting short‐term PV power in a composite climate zone. Finally, the accuracy of the model is compared with other approaches, namely, support vector machine, feedforward neural network, multivariate adaptive regression splines, generalized regression neural network, linear neural network, fuzzy logic‐based model, and empirical models based on multiple regression analysis using statistical performance indicators. The overall analysis reveals that the ANFIS‐based model outperforms the other models in terms of accuracy.

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“…Multiple Linear Regression (MLR) is also popular in PV solar power forecasting (e.g. (De Giorgi et al, 2014), (Oudjana et al, 2012), (Pitalúa-Díaz et al, 2019)). Regression methods establish a relationship between the explanatory (meteorological and geographical) variables and dependent variables (the forecasted PV power) (Das et al, 2018).…”

Section: Ii72 the Use Of Machine Learning In Pv Energy Forecastingmentioning

confidence: 99%

Artificial Intelligence Forecasting Techniques For Reducing Uncertainties In Renewable Energy Applications

AlShafeey¹

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The work presented in this thesis provides an integrated view and related insights for solar and wind farm operators and renewable energy regulators regarding factors influencing electricity production using those resources. The findings help production planning and grid stability improvements through better energy forecasting to reduce uncertainty. With the high increase in energy demand expected in both the near and far future, generating energy from green sustainable resources has now become an imperative necessity. Renewable energy sources like wind and solar are among the most promising and environmentally-friendly energy generation options. However, wind and solar energy production are influenced by many variables which affect the reliability, stability, and economic benefits of wind and solar energy projects, therefore, forecasting the potential amount of energy from wind and solar resources is of great importance. Hence, the objective of the work reported here was to explore the possibility of using artificial intelligence methods to accurately predict the generated renewable power from solar and wind farms based on the available data. Specifically, this thesis reports on the following results: 1. At first, solar photovoltaic (PV) energy forecasting was studied. Operators of grid-connected PV farms do not always have full sets of data available to them, especially over an extended period of time as required by key forecasting techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the work reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm), as well as historical weather data (of the same location) with several key variables, were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 minutes resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively, especially for the structural approach. 2. Then, wind energy forecasting was studied utilizing three machine learning techniques namely Artificial Neural Network (ANN), Support vector machine (SVM), and k-nearest neighbors (K-NN). The three mentioned techniques were used to design, train and test wind energy, forecasting models. Later, a hybrid model was proposed based on these three techniques. Real data obtained from a 2MW grid-connected wind turbine has been used to train and validate the different machine learning techniques. To compare the accuracy of the models over different performance measures with different scales, a comparative evaluation method was devised and used. Results show that the ANN model has great performance in forecasting long-term wind energy, but in contrast, it has very poor short-term performance. SVM model shows better short-term forecasting performance than ANN but presents weak long-term forecasting abilities. K-NN model shows very good short-term forecasting abilities and fair long-term performance. The suggested hybrid model was able to forecast both long and short-term wind energy with very good performance. To that degree, the suggested model can help grid and wind farm operators to forecast the potential amount of wind energy for both long and short term with a good degree of certainty. 3. Finally, the effect of the input data resolution on the forecasting accuracy was studied for both wind and solar. So, datasets were collected from a 546 KWp grid-connected PV farm and a 2 MW wind turbine for one full year. This data was used to train and test Artificial Neural Network models to forecast day-ahead PV and wind energy utilizing time-series input data with 15, 30, and 60 minutes resolutions. The models were able to forecast the PV energy accurately, while the same models trained for wind showed poor performance. Higher input data resolutions lead to slightly better forecasting performance for the PV farm. Utilizing data with higher resolution can improve the forecast by 1-5%. While for wind energy forecasting, the resolution has very minor effects, the 30-minute resolution shows a bit better forecasting performance.

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

Cited by 20 publications

References 22 publications

Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico

Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico

An ANFIS‐based model for solar energy forecasting and its smart grid application

Artificial Intelligence Forecasting Techniques For Reducing Uncertainties In Renewable Energy Applications

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