The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

Qazi, Atika; Fayaz, H.; Wadi, A.; Raj, Ram Gopal; Rahim, N.A.; Khan, Waleed

doi:10.1016/j.jclepro.2015.04.041

Cited by 235 publications

(117 citation statements)

References 47 publications

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“…For example, sizing the projects is related to solar collector and PV systems [2]. Moreover, when solar energy is produced on large-scale and grid-connected, an accurate knowledge of long-term solar radiation makes a lot of sense for balancing the energy supply and demand [3].…”

Section: Introductionmentioning

confidence: 99%

“…Koca et al [31] applied ANN model to the prediction of the monthly mean solar radiation in the Mediterranean region of Anatolia in Turkey by inputting different parameters, and found that the number of the input parameters was the most effective parameter. Generally, the existing ANN model needs a lot of meteorological parameters when applied to radiation prediction to make the results more accurate [3]. The input parameters are basically a certain combination of meteorological and topographical data, which include day of the year, wind speed, rainfall, relative humidity, temperature, latitude, longitude, altitude and so on.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Wang

Qiu

2018

Energies

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Solar radiation prediction is significant for solar energy utilization. This paper presents hybrid methods following the decomposition-prediction-reconfiguration paradigm using only historical radiation records with different combination of decomposition methods, Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Analysis (WA), and the reconfiguration methods, regression model (RE) and Artificial Neural Network (ANN). The application in west China indicates that these hybrid decomposition-reconfiguration models perform well for monthly prediction, while the comparisons of the daily prediction show that the hybrid EEMD-RE model has a higher degree of fitting and a better prediction effect in long-term prediction of solar radiation intensity, which verifies (1) decomposition of original solar radiation data results in components with regular characteristics; (2) the relationship between the original solar radiation sequence and the derived intrinsic mode functions (IMFs) is linear; and (3) EEMD has strong adaptivity for non-linear and non-stationary series. The proposed hybrid decomposition-reconfiguration models have great application prospect for monthly long-term prediction of solar radiation intensity, especially in the areas where complex climate data is difficult to obtain, and the EEMD-RE model is recommended for the daily long-term prediction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Wang

Qiu

2018

Energies

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“…In the scientific literature, there are works that give a comprehensive review of artificial neural networks (ANNs), highlighting their evolution during the years, their mathematical formalism and countless applications in various problems [2][3][4][5][6][7][8][9]. Artificial neural networks are recognized by scientists as efficient forecasting tools as there are a lot of scientific papers that demonstrate their superiority for the forecasting accuracy when compared to other methods like statistical ones [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

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This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

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“…The story feed -forward neural network (one input layer, one hidden layer, and one output layer) is the most commonly used topology in hydrology ( Yadav and Chandel, 2014;Qazi et al, 2015;Rezrazi et al, 2015), as shown in figure 2.1. This topology has proved its ability in modelling many real -world functional problems.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…This investigation used a three-layer or FFNN for H and ET0 simulation (Yadav and Chandel, 2014;Qazi et al, 2015;Rezrazi et al, 2015), where the first layer is the input layer representing input variables, the second layer is the hidden layer, and the third layer is the output layer. This topology has proved its ability in modelling many real-world functional problems (Ata, 2015;Piotrowski et al, 2015;Antonopoulos and Antonopoulos, 2017).…”

Section: Ann Model Architecturementioning

confidence: 99%

Assessment of reference evapotranspiration and global solar radiation in Yucatán Peninsula Mexico

Quej¹,

Hugo²

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The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review

Cited by 235 publications

References 47 publications

Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Assessment of reference evapotranspiration and global solar radiation in Yucatán Peninsula Mexico

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