The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

Behrang, M.A.; Assareh, Ehsanolah; Ghanbarzadeh, Afshin; Noghrehabadi, Aminreza

doi:10.1016/j.solener.2010.05.009

Cited by 286 publications

(135 citation statements)

References 26 publications

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“…(iv) The simulation methodology could be used for optical performance optimization, by comparison with experimental data [35,36]. The approach on solar radiation forecast used in this paper is based on two methods: autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) [38,39].…”

Section: Polymermentioning

confidence: 99%

Trends, Challenges and Opportunities in Advanced Solar Cells Technologies and PV Market

Fara

Diaconu

Drăgan

2016

JGE

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Section: Polymermentioning

confidence: 99%

Trends, Challenges and Opportunities in Advanced Solar Cells Technologies and PV Market

Fara

Diaconu

Drăgan

2016

JGE

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“…The extensive applications of neural networks are pattern recognition and classification, time series prediction and modeling [15]. Neurons which are the basic components of the neural network are interconnected through different layers such as input, hidden and output layers.…”

Section: B Artificial Neural Network Approachmentioning

confidence: 99%

“…In supervised learning, ANN feeds with the learning patterns and adjusts the weights by comparison of the desired output with the actual output obtained from the input variables to achieve the minimum error. On the contrary, unsupervised learning is based on discovering the features of input data in a statistical manner by input irritant classification [15]. In this paper, we focus on the supervised learning.…”

Section: B Artificial Neural Network Approachmentioning

confidence: 99%

The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction

Zahedi¹,

Afzali²,

Afzali³

2012

IJESD

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Abstract-Ambient air temperature prediction is of a concern in environment, industry and agriculture. The increase of average temperature results in natural disasters, higher energy consumption, damage to plants and animals and global warming. Ambient air temperature predictions are notoriously complex and stochastic models are not able to learn the non-linear relationships among the considered variables. Artificial Neural Network (ANN) has potential to capture the complex relationships among many factors which contribute to prediction. The aim of this study is to develop ANN for daily and monthly ambient air temperature prediction in Kerman city located in the south east of Iran. The mean, minimum and maximum ambient air temperature during the years 1961-2004 was used as the input parameter in Feed Forward Network and Elman Network. The values of R, MSE and MAE variables in both networks showed that ANN approach is a desirable model in ambient air temperature prediction, while the results of one day ahead mean temperature and one month ahead maximum temperature are more precise using Elman network.Index Terms-Artificial neural network, feed forward network, elman network, temperature.

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“…The robustness of the DIP, and its comparison with the other commonly used prediction intervals methods, are illustrated in Section 6. In particular, since the available literature on point forecast computation contains a considerable amount of works based on heuristic technique (Mellit and Pavan, 2010;Mellit and Kalogirou, 2008;Sfetsos and Coonick, 2000;Behrang et al, 2010), Section 6 also assesses the performances of the proposed DPI coupled with an ANFIS (adaptive neuro-fuzzy inference system) point forecast model. The main findings of the work and its applicability are summarized in Section 7.…”

Section: Introductionmentioning

confidence: 99%

Model-free computation of ultra-short-term prediction intervals of solar irradiance

2016

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We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities of this DIP are (i) its independence from the method used for the point forecast of solar irradiance, (ii) its independence from the error distribution of the point-forecast method. We compare the DIP with the most common prediction interval methods. By using significant data set covering several months of experimental observations, we have observed higher accuracy and lower width of the prediction intervals of the proposed DIP.

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The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

Cited by 286 publications

References 26 publications

Trends, Challenges and Opportunities in Advanced Solar Cells Technologies and PV Market

Trends, Challenges and Opportunities in Advanced Solar Cells Technologies and PV Market

The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction

Model-free computation of ultra-short-term prediction intervals of solar irradiance

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