ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building

Renno, Carlo; Petito, Fabio; Gatto, Antonio

doi:10.1016/j.jclepro.2016.07.049

Cited by 86 publications

(45 citation statements)

References 53 publications

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“…Xue, günlük yayılan güneş ışınımını tahmininde geri yayılım sinir ağı modelinin etkinliğini ve genelleme kabiliyetini arttırmak için genetik algoritma (GA) ve parçacık sürüsü optimizasyonu (PSO) tekniklerini kullanmıştır [34]. Renno ve arkadaşları, bir konuttaki fotovoltaik uygulaması için günlük global güneş ışınımını ve saatlik direkt güneş ışınımı değerlerini tahmin etmek için iki yapay sinir ağı modelini geliştirmişlerdir [35]. Zou ve arkadaşları, interpolasyon tekniği temelli bir yapay sinir ağı kullanarak Çin'in güneydoğusundaki global güneş ışınımını tahmin etmişlerdir [36].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Arslan

Bayhan

Yaman

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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ÖzBu çalışmada, Nisan 2017 -Mart 2018 tarihleri aralığında Mersin için ölçülen günlük toplam global güneş ışınım değerlerinin yapay sinir ağı kullanılarak modellenmesi yapılmıştır ve literatürde bulunan yaygın modellerin günlük toplam global güneş ışınım değerlerini tahmin etme performansları incelenmiştir. Günlük ortalama hava sıcaklığı, bağıl nem, rüzgar hızı, güneşlenme süresi ve bulut kapalılığı verileri, Devlet Meteoroloji İşleri Genel Müdürlüğü'nden temin edilmiş olup güneş ışınım değerleri ise piranometre ile ölçülmüştür. Sonuç olarak, incelenen modeller içerisinde en iyi tahmin performansını belirlilik katsayısı (R 2 ) 0,83 olan Model 37 (Zhang ve Huang) göstermiştir. AbstractIn this study, the daily total global solar radiation values measured for Mersin between April 2017 and March 2018 were modeled using artificial neural networks and the performance of estimating daily total global solar radiation values of the common models in the literature was investigated. Daily average air temperature, relative humidity, wind speed, sunshine duration, and cloud cover data are obtained from the Turkish State Meteorological Service and solar radiation values are measured with a pyranometer. As a result, Model 37 (Zhang and Huang) showed the best prediction performance among the models examined, with the coefficient of determination (R 2 ) being 0.83.

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Section: Gi̇ri̇ş (Introduction)unclassified

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Arslan

Bayhan

Yaman

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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“…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. [32][33][34]. Thus there exists great limitation when applying ANNs in some areas where meteorological data is hard to obtain.…”

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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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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“…These researches are based on empirical models which are related to some specific variables such as the temperature , the rainfall , the cloudiness , the sunshine duration , the humidity , the elevation , and the latitude . Other researchers have presented study on the estimation of the solar radiation using Artificial Neural Network . Quej et al .…”

Section: Introductionmentioning

confidence: 99%

“…These researches are based on empirical models which are related to some specific variables such as the temperature [8][9][10][11][12], the rainfall [13][14][15], the cloudiness [16,17], the sunshine duration [18][19][20][21][22], the humidity [23,24], the elevation [25,26], and the latitude [22,23]. Other researchers have presented study on the estimation of the solar radiation using Artificial Neural Network [27,28]. Quej et al [29] assesses the accuracy of 13 empirical models to estimate the daily global solar radiation for warm subhumid regions in Yucatán Peninsula, Mexico.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the solar radiation based on air temperature in tunisia

Ayadi

Driss

Bouabidi

et al. 2018

Env Prog and Sustain Energy

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This article focuses on the estimation of the solar global daily radiation using the values of the air temperature. The reason for choosing this environmental parameter is that the data of the air temperature are available worldwide and are easy to be recorded from the local meteorological station. Specified models were developed to estimate the global solar radiation data in considered cities in Tunisia, North Africa. A site‐independent model was proposed to estimate the solar radiation using only the minimum and the maximum daily air temperature. By using five statistical indicators, the proposed models were assessed in order to present the best model for each region in Tunisia. Acceptable values of the indicators were recorded. The presented statistical test is based on recorded data from meteorological stations implemented in Tunis, Kairouan, Sfax, Gabes, and Kebili. The choice of the five selected cities is based on the availability of the metrological measurements. The assessment of the potential of the solar energy in the considered cities was presented. Zoning the assessment of the solar radiation would give a better understanding of the distribution of solar radiation and an overview of the general solar radiation climatic conditions within Tunisia. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 600–607, 2019

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ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building

Cited by 86 publications

References 53 publications

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

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

Estimation of the solar radiation based on air temperature in tunisia

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