Application of ANN model to predict the performance of solar air heater using relevant input parameters

Ghritlahre, Harish Kumar; Chandrakar, Purvi; Ahmad, Ashfaque

doi:10.1016/j.seta.2020.100764

Cited by 32 publications

(18 citation statements)

References 38 publications

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“…The importance of input parameters on the output of the model is evaluated by using sensitivity analysis based on the Cosine Amplitude Method (CAM). The strength of the relationship between output and input parameters is acquired by using the following equation [36]: where N is the number of data samples, x ik is the input parameter and x jk is the output parameter. The value of Rij lies between 0 and 1.…”

Section: Resultsmentioning

confidence: 99%

“…The value of Rij lies between 0 and 1. If the value of R ij is found to be 0, then no relationship will be observed between input and output parameter, if the value is found as nearer to 1 then the strong relationship will be observed between input and output parameter [36]. The importance of input variables of the proposed ANN model is provided in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

GONCU

Köroğlu

OZDIL

2021

Journal of Thermal Engineering

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Since global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy.The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network's hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 5.8262x10 7 W/m 2 for the testing data set. The ANN's testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

GONCU

Köroğlu

OZDIL

2021

Journal of Thermal Engineering

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“…However, the hourly and daily energy consumption distribution does not follow any simple proportional relationship with these parameters and cannot be described by simple mathematical formulas. Their relationship can be analyzed and predicted by higher-level data mining methods, such as simulation [37][38][39][37], artificial neural network algorithms [40][41][42].…”

Section: Fig 12 Energy Consumption Partial Correlation Coefficient In...mentioning

confidence: 99%

Analysis to energy consumption characteristics and influencing factors of terminal building based on airport operating data

Xie

Luo

et al. 2021

Sustainable Energy Technologies and Assessments

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“…For instance, machine learning algorithms were used to predict and monitor the content of haloketones (HKS) and trihalomethanes (THMS) (Deng et al, 2021;Hong et al, 2020). Also, machine learning algorithm was applied in predicting and assessing the performance of solar air heaters and solar collector systems (Ghritlahre and Prasad, 2018a;Ghritlahre et al, 2020). Moreover, the thermal conductivity and viscosity of nanofluid were predicted by using machine learning algorithms (He et al, 2020;Toghraie et al, 2019).…”

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confidence: 99%

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

JIN

Zhu

2022

JTST

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Application of machine learning algorithms for the predictionof flame temperature in small-scale burner fueled with ethanol-diesel fuel blends IntroductionAs a light petroleum product, diesel fuel is widely used in ships, large-scale vehicles, and locomotives in railway. However, contaminants such as oxides of nitrogen (NOx) and particulate matter (PM) are generated with the use of diesel fuel. It was found that diesel fuel can be partially replaced by oxygenated fuels, reducing contaminant emissions from combustion. Consequently, many researchers have devoted their attention to the study of oxygenated fuels, such as methanol, ethanol and biodiesel (Hansen et al., 2005;Moon et al., 2010;Yao et al., 2010). Ethanol contains oxygen in 34.7wt% and its required air is theoretically nearly half as diesel when burned (Lu et al., 2021). Therefore, diesel fuel blended with ethanol can make combustion more fully, which reduces soot emissions.Ethanol-diesel fuel blends requires appropriate equipment to achieve efficient combustion. The small-scale burner is the key equipment of small-electromechanical system and this burner has the advantage of high energy density. However, in the small scale, many new problems and challenges arise, such as incomplete combustion, unstable flame, and large heat loss (

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Application of ANN model to predict the performance of solar air heater using relevant input parameters

Cited by 32 publications

References 38 publications

Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

Analysis to energy consumption characteristics and influencing factors of terminal building based on airport operating data

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

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