New technique for estimating the monthly average daily global solar radiation using bees algorithm and empirical equations

Tolabi, Hajar Bagheri; Ayob, Shahrin Md.; Moradi, Mohammad H.; Shakarmi, Mehrnoosh

doi:10.1002/ep.11858

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…Solar energy has received significant attention among the renewables and is considered to be a worthy solution to the world's dependence on conventional oil and gas based fuels . In the current energy scenario, the abundant amount of energy (i.e., solar radiation) on the surface of earth at no cost can be of strong advantage to reduced dependence on conventional fuels .…”

Section: Introductionmentioning

confidence: 99%

Solar radiation on south‐facing inclined surfaces under different climatic zones in India

Jamil

Siddiqui²,

Denkenberger

2018

Env Prog and Sustain Energy

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In this work, solar radiation availability has been estimated for south‐facing inclined surfaces in India. Five different climatic zones prescribed by Energy Conservation Building Code (ECBC) for India have been considered. A comparison of optimum tilt angles and corresponding incident solar radiation for representative locations under different climatic zones, that is, Srinagar (cold), Delhi (composite), Jodhpur (hot and dry), Mumbai (warm and humid), and Bangalore (Temperate) has been established. Gains in the availability of incident solar radiation in comparison with a horizontal surface observed for Jodhpur and Bangalore were found to be maximum (15.77%) and minimum (7.57%), respectively, on monthly optimum tilt angle. A loss of 5.74% for cold climate, 7.49% for composite climate, 6.16% for hot and dry climate, 4.30% for warm and humid climate, and 4.39% for temperate climate were estimated with surface tilted at annual optimum tilt in comparison with a surface at monthly optimum tilt angle. A linear relationship was observed between annual optimum tilt angles and locations latitude as βopt(annual) = 0.671 ϕ + 9.087 with R2 = 0.9984. It was concluded that climatic conditions can considerably affect the availability of incident solar radiation under different climatic zones. ‘ 2018 American Institute of Chemical Engineers Environ Prog, 38:e13050, 2019

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Section: Introductionmentioning

confidence: 99%

Solar radiation on south‐facing inclined surfaces under different climatic zones in India

Jamil

Siddiqui²,

Denkenberger

2018

Env Prog and Sustain Energy

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“…The empirical approach, which is the most widely used, applies regression models for analyzing the correlations between various meteorological variables, such as sunshine duration, cloudiness, and temperature [ 22 ]. Empirical models are simple and intuitive, but their accuracy is insufficient for practical use [ 23 , 24 , 25 ]. The statistical approach predicts solar irradiance based on temporal correlations between historical meteorological variables using statistical models, such as the autoregressive moving average model (ARMA) [ 26 ] and autoregressive integrated moving average model (ARIMA) [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

Jeon

Choi

Lee

2022

Sensors

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Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.

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“…In fact, these tools are available at some specific sites across Tunisia because of the high cost. Researchers presented a lot of correlations to estimate the solar radiation from available meteorological data due to the absence of the solar radiation data with reliable qualities . 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 .…”

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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New technique for estimating the monthly average daily global solar radiation using bees algorithm and empirical equations

Cited by 13 publications

References 29 publications

Solar radiation on south‐facing inclined surfaces under different climatic zones in India

Solar radiation on south‐facing inclined surfaces under different climatic zones in India

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network

Estimation of the solar radiation based on air temperature in tunisia

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