Daily total global solar radiation modeling from several meteorological data

Bilgili, Mehmet; Özgören, Muammer

doi:10.1007/s00703-011-0137-9

Cited by 42 publications

(11 citation statements)

References 42 publications

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“…Air quality in the urban areas is assessed through a combination of various meteorological factors [1], including solar energy, the main driver of life on Earth. Various authors [2][3][4] have noted that solar energy influences all the physical, chemical and biological processes operating in terrestrial ecosystem due to its role in energy and water vapor balance. For example, solar energy plays a controlling role in plant growth, soil heating and evapotranspiration.…”

Section: Introductionmentioning

confidence: 99%

Effects of Sky Conditions Measured by the Clearness Index on the Estimation of Solar Radiation Using a Digital Elevation Model

Alves¹,

Sanches²,

Nogueira³

et al. 2013

ACS

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This study evaluated the effects of sky conditions (measured by the clearness index, K T) on the estimation of solar radiation and its components. Solar radiation was calculated by a digital elevation model derived from the Shuttle Radar Topography Mission (SRTM). The calculated radiation was parameterized and validated with measured solar radiation from two stations inside the urban perimeter of the city of Cuiabá, Brazil, during 2006 to 2008. The measured solar radiation varied seasonally, with the highest values in December-March and the lowest in June-September. Comparisons between calculated and measured values for two sites in Cuiabá demonstrate that the model is accurate for daily Rg estimates under clear sky conditions based on Root Mean Square Error, Mean Bias Error and Willmott's index. However, under partially cloudy and cloudy sky conditions the model was not able to provide robust estimates. Spatially, the highest values of incident Rg occurred on strands with North, Northeast and Northwest orientations and were lowest on those oriented to the South, Southeast and Southwest.

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Section: Introductionmentioning

confidence: 99%

Effects of Sky Conditions Measured by the Clearness Index on the Estimation of Solar Radiation Using a Digital Elevation Model

Alves¹,

Sanches²,

Nogueira³

et al. 2013

ACS

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“…Similar ANN networks have also been applied elsewhere to predict solar radiation and wind speed using many models with different input parameters and numbers of neurons. Some results of predictions done of other countries that have different topographies (maximum and minimum asset values of MAPE) are: 4.6% and 3.4% in Indonesia , 11% and 3% in Iran , 10.02% and 5.21% in Iran , 12.5% and 0.03% in India , 25.3% and 8.9% in Nigeria , 6.73% and 1.72% in Turkey , 19.1% and 6.5% in Saudi Arabia , and 18.5% and 9.23% in Turkey . In comparison the results of this study have maximum and minimum errors of 6.07% and 3.2% respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Past research has shown that a relatively simple neural network can fit any practical function . Therefore, many solar radiation prediction models have been developed using ANN . Researchers often use this technique with deferent parameters for input, such as: longitude, latitude, number of days, temperature, altitude, humidity, average diffused radiation, mean beam radiation, month, humidity, relative humidity, sunshine hours and wind speed, wave length and rainfall.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 99%

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

Al-Fatlawi

Rahim

Saidur

et al. 2015

Env Prog and Sustain Energy

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This research assesses the feasibility of using artificial neural networks (ANN) to predict and improve the spatial distribution of solar radiation data, using Peninsular Malaysia as a case study. This peninsula has seas to the east and west that control cloud formation and rain throughout the year. A rugged mountain range bisects the length of the peninsula creating a complex topography. These features make it difficult to develop effective empirical solar radiation models to cover large areas in Peninsular Malaysia. In this article, several different solar radiation prediction models were designed using the ANN tool in MATLAB. Geographical and meteorological data from 24 solar energy stations were used to predict the solar radiation in 341 cities. Standard multilayer, feed‐forward, and back‐propagation neural networks were used for the 12 solar radiation models with different numbers of neurons, training functions and activation functions. Predicted solar radiation results were actively used to develop monthly solar radiation maps. The results show that the mean absolute percentage error is less than 6.07% for both the training and testing datasets. This shows that the models are highly reliable predictors of solar radiation values, even in the selected locations that have deficient or unavailable solar radiation databases. The maps show that Peninsular Malaysia receives a monthly average daily solar radiation of between 3.82 and 5.23 kWh/m2‐day, and that the extreme northern region in Peninsular Malaysia has the highest solar radiation intensity throughout the year. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1528–1535, 2015

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“…Analitik ve doğrusal olmayan, hareketsiz veya stokastik türler gibi karmaşık problemler YSA'lar kullanıldığında sınırlı programlama bilgisi ile çözülebildiği gibi özellikle programda yeniden programlama veya başka girişimler olmaksızın çeşitli sorunlar da çözülebilir [35]. Yapay sinir ağları yönetiminde farklı ağ yapıları bulunmaktadır.…”

Section: A Yapay Si̇ni̇r Ağlariunclassified

YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması

Ekinci

2019

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Enerji tüketimi, ülkelerin sosyal ve ekonomik gelişmişliğini gösteren en önemli faktörlerden biridir. Son yıllarda, Türkiye'nin enerji talebi de yaşanan ekonomik ve sosyal büyüme ile birlikte artmaktadır. Artan enerji talebinin planlanması ve yönetilmesi ülkenin enerji politikası için hayati öneme sahiptir. Enerjinin planlaması ve yönetiminin doğru ve güvenilir olması ekonomik ve doğal kaynakların etkin kullanılmasını sağlayacaktır. Enerji planlanması ve yönetiminde tahmin yöntemleri ve algoritmalar enerji talebinin belirlenmesinde kullanılan en yaygın yöntemlerdir. Elektrik enerjisi talebi bölgesel, mevsimsel ve anlık dalgalanmalar gösterebilmektedir. Bu nedenle, tahmine etki eden faktörlerin doğru belirlenmesi ve değerlendirilmesi gerekir. Yaşanan ekonomik büyüme birlikte artan konut ihtiyacı da enerji talebini artırmaktadır. Bu çalışmada, 1970-2015 yıllarına ait Türkiye elektrik enerjisi verileri işlenmiş ve Yapay Sinir Ağları (YSA) ve Adaptif Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi (ANFIS) yöntemleri ile yapılan tahminler gerçekleşen tüketim değerleri karşılaştırılarak iki yöntemin performans analizi yapılmıştır.

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Daily total global solar radiation modeling from several meteorological data

Cited by 42 publications

References 42 publications

Effects of Sky Conditions Measured by the Clearness Index on the Estimation of Solar Radiation Using a Digital Elevation Model

Effects of Sky Conditions Measured by the Clearness Index on the Estimation of Solar Radiation Using a Digital Elevation Model

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

YSA VE ANFIS Tekniklerine Dayalı Enerji Tüketim Tahmin Yöntemlerinin Karşılaştırılması

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