Empirical models for estimating the global solar radiation of Jubail Industrial City, the Kingdom of Saudi Arabia

Mujabar, P. Sheik; Venkateswara, Rao Chintaginjala

doi:10.1007/s42452-020-04043-9

Cited by 17 publications

(9 citation statements)

References 34 publications

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“…The Kingdom of Saudi Arabia is located in the world's sun-belt at 36.6-50 • E longitudes and 17.5-31 • N latitudes with an annual average solar radiation of 2200 kWh/m 2 [100]. Hot water demand is an important part of energy consumption in the residential sector of this country, where electric water heaters consume the majority of the energy, especially in the wintertime.…”

Section: Saudi Arabiamentioning

confidence: 99%

A Review on Techno-Economic Assessment of Solar Water Heating Systems in the Middle East

et al. 2021

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Currently, the economy of Middle Eastern countries relies heavily on fossil fuel sources. The direct and indirect adverse consequences of fossil fuel utilization for power generation enforce the region’s countries to raise the share of renewable energy. In this context, various incentive policies have been developed to encourage the residential and industrial sectors to support a portion of energy needs through renewable energy resources. In this case, a solar water heating system (SWHS) as an application of solar thermal technology provides some of the heat energy requirements for domestic hot water (DHW) and space heating, supported conventionally by electricity or natural gas, or even other fossil fuels. This paper reviews the feasibility of the SWHS in the Middle East region from technical and economical standpoints and investigates some of the progress, challenges, and barriers toward this market. The pay-back times and CO2 emission reduction under different incentive frameworks and configurations of each system have been assessed in this context. Furthermore, the advantages and weaknesses of the SWHS in several countries have been reported. Finally, various guidelines have been proposed to enhance the development of this technology.

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Section: Saudi Arabiamentioning

confidence: 99%

A Review on Techno-Economic Assessment of Solar Water Heating Systems in the Middle East

et al. 2021

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“…They developed three regression models comprising relative humidity and temperature as inputs variables and GHI as an output variable. In another similar work, three regression models were proposed for global solar radiation prediction [22], in which three features, namely ambient temperature, relative humidity, and sunshine hours, were taken as independent variables. It is observed that, compared to the linear model, the quadratic model provides better prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

et al. 2021

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Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2.

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“…The most traditional models incorporating maximum and minimum temperatures are the Hargreaves and Samani and Bristow and Campbell models [4,10]. Both models are the basis of innovative approaches adapted to specific location conditions [7,12,27,[29][30][31][32].…”

Section: Empirical Temperature-based Modelsmentioning

confidence: 99%

A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

Hoyos-Gómez

Ruiz-Mendoza

2021

Applied Sciences

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Solar irradiance is an available resource that could support electrification in regions that are low on socio-economic indices. Therefore, it is increasingly important to understand the behavior of solar irradiance. and data on solar irradiance. Some locations, especially those with a low socio-economic population, do not have measured solar irradiance data, and if such information exists, it is not complete. There are different approaches for estimating solar irradiance, from learning models to empirical models. The latter has the advantage of low computational costs, allowing its wide use. Researchers estimate solar energy resources using information from other meteorological variables, such as temperature. However, there is no broad analysis of these techniques in tropical and mountainous environments. Therefore, in order to address this gap, our research analyzes the performance of three well-known empirical temperature-based models—Hargreaves and Samani, Bristol and Campbell, and Okundamiya and Nzeako—and proposes a new one for tropical and mountainous environments. The new empirical technique models daily solar irradiance in some areas better than the other three models. Statistical error comparison allows us to select the best model for each location and determines the data imputation model. Hargreaves and Samani’s model had better results in the Pacific zone with an average RMSE of 936,195 Wh/m2 day, SD of 36,01%, MAE of 748,435 Wh/m2 day, and U95 of 1.836,325 Wh/m2 day. The new proposed model showed better results in the Andean and Amazon zones with an average RMSE of 1.032,99 Wh/m2 day, SD of 34,455 Wh/m2 day, MAE of 825,46 Wh/m2 day, and U95 of 2.025,84 Wh/m2 day. Another result was the linear relationship between the new empirical model constants and the altitude of 2500 MASL (mean above sea level).

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Empirical models for estimating the global solar radiation of Jubail Industrial City, the Kingdom of Saudi Arabia

Cited by 17 publications

References 34 publications

A Review on Techno-Economic Assessment of Solar Water Heating Systems in the Middle East

A Review on Techno-Economic Assessment of Solar Water Heating Systems in the Middle East

Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

A New Empirical Approach for Estimating Solar Insolation Using Air Temperature in Tropical and Mountainous Environments

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