An integrated artificial neural networks approach for predicting global radiation

Azadeh, A.; Maghsoudi, Ali; Sohrabkhani, S.

doi:10.1016/j.enconman.2009.02.019

Cited by 115 publications

(37 citation statements)

References 19 publications

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“…En la Tabla 4 se resumen los parámetros estadísticos de las regresiones. Al comparar los resultados con los presentados por otras investigaciones en las que se emplean parámetros meteorológicos alternos como variables de entrada en las regresiones [27], [31], [32], los valores del presente trabajo se encuentran por debajo de los presentados, los cuales varían entre 95 y 99% en el coeficiente R 2 . Sin embargo, los valores presentados de R 2 en las regresiones múltiples son más altos que las regresiones de una sola variable, esto se puede atribuir al hecho que las condiciones de medición de la radiación solar, se pueden ver influenciadas por los entornos meteorológicos del área de estudio.…”

Section: Resultados Y Discusiónunclassified

Evaluación de un modelo estadístico para estimar la radiación solar en Magdalena, Colombia

Vélez-Pereira

Vergara-Vásquez

Barraza-Coronell

et al. 2015

TecnoL.

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Section: Resultados Y Discusiónunclassified

Evaluación de un modelo estadístico para estimar la radiación solar en Magdalena, Colombia

Vélez-Pereira

Vergara-Vásquez

Barraza-Coronell

et al. 2015

TecnoL.

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“…Some examples are [89] applied time delayed neural network; [90] Many researchers publish forecasting results with new data from various regions in the world for archive purpose [97] used MLPs for forecasts for six cities in Iran [98]. Forecast solar irradiance of a grid connected PV plants in Italy.…”

Section: Fig 2: Classification Of Different Modelsmentioning

confidence: 99%

Solar Power Forecasting: A Review

K.¹,

Isha²

2016

IJCA

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The increasing demand for energy is one of the biggest reasons behind the integration of solar energy into the electric grids or networks. To ensure the efficient use of energy PV systems it becomes important to forecast information reliably. The accurate prediction of solar irradiance variation can enhance the quality of service. This integration of solar energy and accurate prediction can help in better planning and distribution of energy.Here in this paper, a deep review of methods which are used for solar irradiance forecasting is presented. These methods help in selecting the appropriate forecast technique according to the needs or requirements. This paper also presents the metrics that are used for evaluating the performance of a forecast model.

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“…The direct and diffuse horizontal irradiance can be calculated according to the following steps [15,16]: according to the Cooper formula, the declination angle is calculated by the following equation:…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…ANN [6,[14][15][16] is the most popular and widely used tool for artificial intelligence modeling. It is formed by a large numbers of highly related processing elements through an adaptive learning process.…”

Section: Artificial Neural Network (Ann) Fundamentalsmentioning

confidence: 99%

A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks

Zhu

Sun

et al. 2015

Energies

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Abstract:The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power's periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (ANN) to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method described in this paper needs less calculation time and has better forecasting precision.

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An integrated artificial neural networks approach for predicting global radiation

Cited by 115 publications

References 19 publications

Evaluación de un modelo estadístico para estimar la radiación solar en Magdalena, Colombia

Evaluación de un modelo estadístico para estimar la radiación solar en Magdalena, Colombia

Solar Power Forecasting: A Review

A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks

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