Comparison of Different Models for Estimation of Global Solar Radiation in Jharkhand (India) Region

Namrata, Kumari; Sharma, Sandeep; Saksena, S. B. L.

doi:10.4236/sgre.2013.44041

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…The result of the linear fitting of the scatter diagram is given by equation (13). 0 = 0.5 + 0.11( 0).…”

Section: Results Of Linear Fitting Of Against Plotmentioning

confidence: 99%

Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Tsung

2020

JEST

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Electricity demands are on the rise and with it, carbon dioxide emissions from many conventional power plants are increasing. In the efforts to mitigate such phenomena, the Malaysian government seeks to implement Building Integrated Photovoltaic (BIPV) projects. Early stage studies on Global Solar Radiation (GSR) have been carried out in several states in Malaysia including Penang, Kuala Lumpur and Kota Bharu. Afterward, data from the Malaysia Meteorological Department and the Malaysia National University have been used to estimate the monthly average daily global radiation in various locations in Malaysia. Putrajaya, a location which is implementing Malaysian Building Integrated Photovoltaic (MBIPV) is among the locations where a GSR study is currently absent. Conventional methods exist for GSR estimation with the aid of pyranometer. However, this method of GSR estimation is time consuming and not cost-effective practice. The main objective of this study is to estimate the GSR in Putrajaya. This is achieved in this study by utilizing sunshine-based data with calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine to plot a linearly fitted graph. Coefficients in the Angstrom-Prescott (A-P) model was generated from the plotted graph and was used for GSR estimation where a = 0.5 and b = 0.11. The mean percentage error (MPE) of the GSR estimation was found to be 3.4. Therefore, the estimation of GSR in Putrajaya have been successful for the first-time using sunshine-based data from dual locations method. The GSR estimation of Putrajaya in this study could benefit stakeholders in civil development sectors, policy and energy authorities.

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“…The result of the linear fitting of the scatter diagram is given by equation (13). 0 = 0.5 + 0.11( 0).…”

Section: Results Of Linear Fitting Of Against Plotmentioning

confidence: 99%

Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Tsung

2020

JEST

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“…Ranchi (India) is a good site to profit from the benefits of solar energy related researches and deployment of their related technologies because it is close to the Tropic of Cancer. 27 Maximum solar energy can be harnessed here.…”

Section: Site Selections and Data Descriptionmentioning

confidence: 99%

“…According to the most recent Jharkhand Renewable Energy Development Agency (JRDEA) study, the state receives average daily solar irradiance of 4.5–5.5 kWh/m 2 , which allows for the deployment of suitable solar power infrastructure 26 . We have chosen location as Ranchi because of the following reasons: Ranchi (India) is a good site to profit from the benefits of solar energy related researches and deployment of their related technologies because it is close to the Tropic of Cancer 27 . Maximum solar energy can be harnessed here. Many research papers have been published for solar radiation prediction for Ranchi, India region.…”

Section: Site Selections and Data Descriptionmentioning

confidence: 99%

Hyper‐parametric improved machine learning models for solar radiation forecasting

Kumar

Namrata

Kumari

2022

Concurrency and Computation

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Summary Spatiotemporal solar radiation forecasting is extremely challenging due to its dependence on metrological and environmental factors. Chaotic time‐varying and non‐linearity make the forecasting model more complex. To cater this crucial issue, the paper provides a comprehensive investigation of the deep learning framework for the prediction of the two components of solar irradiation, that is, Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI). Through exploratory data analysis the three recent most prominent deep learning (DL) architecture have been developed and compared with the other classical machine learning (ML) models in terms of the statistical performance accuracy. In our study, DL architecture includes convolutional neural network (CNN) and recurrent neural network (RNN) whereas classical ML models include Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and K‐Nearest Neighbor (KNN). Additionally, three optimization techniques Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO) have been incorporated for tuning the hyper parameters of the classical ML models to obtain the best results. Based on the rigorous comparative analysis it was found that the CNN model has outperformed all classical machine learning and DL models having lowest mean squared error and highest R‐Squared value with least computational time.

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“…Chih-Chiang Wei 21 presented evaluation models to forecast the sun energy radiation on tilted surfaces. In 22 , 23 a simple correlation is presented to compute the mean monthly daily global radiation. Qasaimeh et al 24 implemented a study in Jordon to compute the OTAs and they concluded that their values are limited between 10° and 60°.…”

Section: Introductionmentioning

confidence: 99%

Estimation of optimal tilt angles for photovoltaic panels in Egypt with experimental verifications

Abdelaal

El‐Fergany

2023

Sci Rep

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The principal target of this work is to compute the optimal tilt angle (OTA) for Photovoltaic (PV) panels. To perform this task, comprehensive simulations are done starting from altering the tilt angle (TA) daily, to use one fixed TA for all the year. The mathematical models for extra-terrestrial radiation (ETR) of both horizontal and inclined surfaces are presented firstly. At a later stage, the optimization formulation for the maximizing the solar radiation (SR) is adapted, and then the daily, monthly, seasonally, half-yearly and optimal fixed TAs are obtained. Although, the daily OTA produces the maximum SR, it is costly and impractical. It is found that altering the TA twice a year at optimal values that are computed as 5° and 50° for Suez city, gives the best results that are very near to the daily altering of the OTA. The difference between the two methods is 1.56% which is very small. Also, the two OTAs has SR better than that of the fixed OTA which is 28° by 7.77%. Also, it is found that the yearly fixed OTA (28°) is nearly equal to the latitude angle of Suez city which is 30°. The two OTAs method of this paper is different from the commonly used method that suggests two TAs. The first TA is used for winter months which is obtained by adding 15° to the latitude angle while the second TA is obtained by subtracting 15° from the latitude angle for the summer months. This commonly used method produces lesser SR than the two OTAs method of this paper. The theoretical work has been proved by an experimental work on two PV systems constructed at 25° and 30° TAs. The results of the experimental work agree with the theoretical results.

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Comparison of Different Models for Estimation of Global Solar Radiation in Jharkhand (India) Region

Cited by 21 publications

References 13 publications

Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Hyper‐parametric improved machine learning models for solar radiation forecasting

Estimation of optimal tilt angles for photovoltaic panels in Egypt with experimental verifications

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