Generation of common coefficients to estimate global solar radiation over different locations of India

Samanta, Suman; Patra, Pulak Kumar; Banerjee, Saon; Narsimhaiah, Lakshmi; Chandran, M. A. Sarath; Kumar, Pradeep; Bandyopadhyay, Sanjib

doi:10.1007/s00704-018-2531-4

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…Estimating solar radiation requires models and empirical correlations. The Gopinathan correlation, which was chosen from 13 solar radiation models and correlations, is more applicable for Vellore [20][21][22]. Vellore's 2018 solar radiation was calculated in MATLAB R2020a using Gopinathan correlation and climatic data from Brahmapuram, Vellore, on middays each month (defined by Klien [23]).…”

Section: Solar Radiation Analysismentioning

confidence: 99%

Extensive Analysis of a Reinvigorated Solar Water Heating System Using Low-Density Polyethylene Glazing

Duraivel,

Muthuswamy,

Shaik

et al. 2023

Energies

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Solar energy is one of the most promising forms of alternative energy because it has no adverse effects on the environment and is entirely free. Converting solar energy into thermal energy is the most common and straightforward method; the efficiency of solar thermal conversion is approximately 70 percent. The intermittent nature of solar energy availability affects the performance of solar water heaters (SWH), which lowers the usefulness of solar energy in residential and commercial settings, particularly for water heating. Even at low temperatures, the performance of a collector can be improved by using low-density polyethylene (LDPE) glazing instead of traditional glass because it is less expensive and lighter than glass. Using a comprehensive experimental-simulative study, the Glass Solar water heater (glass SWH) and the low-density polyethylene solar water heater (LDPE SWH) are analyzed, examined, and compared in this work. These solar water heaters have galvanized iron (GI) as their absorber material. The SWHs were operated in a closed loop at a constant mass flow rate of 0.013 kg/s, and a 4E analysis (which stands for energy, exergy, economics, and efficiency recovery ratio) was carried out. This analysis included a look at the dynamic time, uncertainty, weight reduction, carbon footprint, and series connection. An LDPE SWH has an energy efficiency that is 5.57% and an exergy efficiency that is 3.2% higher than a glass SWH. The weight of the LDPE SWH is 32.56% lower than that of the glass SWH. Compared to the price of a conventional geyser, installing our SWH results in a cost savings of 40.9%, and monthly energy costs are reduced by an average of 25.5%. Compared to October, September has the quickest dynamic time to reach the desired temperature, while October has the most significant dynamic time. The efficiency recovery ratio (ERR) of a glass SWH is 0.0239% lower than that of an LDPE SWH. LDPE SWHs had a carbon credit worth INR 294.44 more than glass SWHs. The findings of these tests demonstrate that the LDPE SWH is a practical replacement for traditional means of heating water, such as SWHs and geysers.

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Section: Solar Radiation Analysismentioning

confidence: 99%

Extensive Analysis of a Reinvigorated Solar Water Heating System Using Low-Density Polyethylene Glazing

Duraivel,

Muthuswamy,

Shaik

et al. 2023

Energies

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“…and Sandeep (2019),Monteiro et al (2019),Samanta et al (2019), Shahrukh Anis et al (2019,Tsung et al (2019), Woldegiyorgis (2019),Argungu et al (2020),Kaplan and Kaplan. (2020),Nadjem et al (2020),Saud et al (2020) S2H H 0 [a + b( S S0 ) + c( S S0 ) 2 ] Ogelman et al(1984),Rietveld (1978),Akinoǧlu and Ecevit (1990), Layi Fagbenle (1993),Aksoy (1997),Elagib et al (1999), Togrul et al (2000b, Toğrul, 2002, Almorox andHontoria (2004),Jin et al (2005),Aras, Balli, and Hepbasli (2006),Bakirci (2009),Katiyar and Pandey (2010),Duzen and Aydin (2012),Assi and Jama (2010),Sekhar et al (2013),Suthar et al (2014),Onyango and Ongoma (2015),Ozturk (2015),Akinnawo et al (2016),Sarkar and Sifat (2016), Liu et al (2016), Gong et al (2016), Yang et al (2016), Ayodele and Ogynjuyigbe (2017), Achour et al (2017), Bakirci (2017), Cao et al (2017), Aoun and Bouchouicha (2017), Kaplan (2018), Benamrou et al (2018), Siva Krishna Rao et al (2018), Olatona…”

mentioning

confidence: 99%

“…,Uckan and Khudhur (2018),Kada et al (2018),Zhang et al (2018),Akpootu et al (2019),Ishola et al (2019),Khan and Ahmad (2019),Manju and Sandeep (2019),Pant et al (2019),Samanta et al (2019), Shahrukh Anis et al (2019,Kaplan and Kaplan. (2020),Nadjem et al (2020),Saud et al (2020),Xiao et al (2020) S3H H 0 [a + b( S S0 ) + c( S S0 ) 2 + d( S S0 ) 3 ] Bahel et al (1987),Samuel (1991),Lewis (1992), Togrul et al (2000b, Toğrul, 2002, Rensheng et al (2006),Ulgen and Hepbasli (2004),Almorox and Hontoria (2004),Jin et al (2005),Aras et al (2006),Rensheng et al (2006),Katiyar and Pandey (2010),Duzen and Aydin (2012),Assi and Jama (2010),Ouali and Alkama (2014),Onyango and Ongoma (2015),Ozturk (2015),Akinnawo et al (2016),Sarkar and Sifat (2016),Liu et al (2016),Gong et al (2016),Yang et al (2016),Ayodele and Ogynjuyigbe (2017),Kaplan (2018), Achour et al (2017), Bakirci (2017), Cao et al (2017), Benamrou et al (2018), Gouda et al (2018), Uckan and Khudhur (2018), Kada et al (2018), Zhang et al (2018), Siva Krishna Rao et al (2018), Akpootu et al (2019), Khan and Ahmad (2019), Manju and Sandeep (2019), Pant et al (2019), Pant et al (2019), Samanta et al (2019), Feng et al (2020), Kaplan and Kaplan.…”

mentioning

confidence: 99%

The applicability of sunshine-based global solar radiation models modified with meteorological factors for different climate zones of China

et al. 2023

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With the development of renewable energy, the exploitation and utilization of solar energy resources also need continuous progress, but solar radiation data shortage has become a serious concern. A method for estimating global solar radiation has been developed to address this issue. The sunshine-based model is currently the most widely used model due to its high calculation accuracy and few input parameters. This paper will first review 13 subcategories (8 categories in total) of the global solar radiation prediction model based on sunshine. Subsequently, the astronomical factors were introduced to modify empirical coefficients, and 8 new categories of models based on sunshine rate were introduced. The radiation data from 83 meteorological stations in China was used to train and validate the model, and the performance of the model was evaluated by using evaluation indicators, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute bias error (MABE), mean bias error (MBE), and global performance index (GPI). The results show that the R2 value of the unmodified empirical model is in a range of 0.82–0.99, and the RMSE value is in a range of 0.018–3.09. In contrast, with the introduction of the astronomical factor, the model accuracy improves significantly, and the modified power function model (N3) gains its best performance. The R2 of model N3 is in a range of 0.86–0.99, and the RMSE value is in a range of 0.018–2.62. The R2 increases by 0.49%, while the RMSE value 6.44%. Above all, it does not require the input of other meteorological parameters for predicting the value of global solar radiation.

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Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data

Banerjee¹,

Singal

Saha³

et al. 2022

Int J Biometeorol

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Generation of common coefficients to estimate global solar radiation over different locations of India

Cited by 9 publications

References 44 publications

Extensive Analysis of a Reinvigorated Solar Water Heating System Using Low-Density Polyethylene Glazing

Extensive Analysis of a Reinvigorated Solar Water Heating System Using Low-Density Polyethylene Glazing

The applicability of sunshine-based global solar radiation models modified with meteorological factors for different climate zones of China

Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data

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