Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey

Dombaycı, Ö. Altan; Gölcü, Mustafa

doi:10.1016/j.renene.2008.07.007

Cited by 103 publications

(32 citation statements)

References 16 publications

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“…In classical ANNs, the training of the weights is accomplished using Gradient Descent (GD), a gradient based algorithm. Recently, however, Levenberg-Marquardt (LM) algorithm has instead been used for the training purpose as it has shown to outperform GD in a variety of problems [7,8,9,10]. As LM is still a gradient based technique, it can still converge to the local minimum depending on the initial weight values.…”

Section: Modellingmentioning

confidence: 99%

Modelling of Decision Making in the Production of Stator Core Using a GA-ANN Approach

Rajora¹,

Zou²,

Xu³

et al. 2017

dtcse

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Abstract. In the production of stator cores, it is relied upon experienced engineers to make time sensitive decisions on the number of compensation sheets to be added to achieve uniform pressure distribution though out the laminations. However, this method yields inconsistent results as humans are unable to store and analyze large amounts of data. In this paper ANNs have been employed to help the engineers with the decision making process. The ANNs are trained using a hybrid Genetic Algorithm (GA) -Levenberg-Marquardt (LM) to avoid local convergence. When used on testing data sets, the ANNs displayed a high degree of prediction accuracy indicating their ability to simulate the decision making process of these experienced engineers.

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Section: Modellingmentioning

confidence: 99%

Modelling of Decision Making in the Production of Stator Core Using a GA-ANN Approach

Rajora¹,

Zou²,

Xu³

et al. 2017

dtcse

View full text Add to dashboard Cite

show abstract

“…Then, error that is the difference between the network output and the desired output is found. This training cycle is repeated until the error reduces to an acceptable value [6].…”

Section: Figmentioning

confidence: 99%

“…The results show that this study can be helpful to concentrate on the trend of weather over a long period of time in a particular station and area. In same manner, Dombaycı and Gölcü [6] used ANN in daily temperature forecasting and evaluated ANN accuracy using FV and RMSE. Smith et al [7] used ANN to forecast air temperature based on near real-time data.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Models to Daily Average Temperature Analysis

Göçken

Boru

Dosdoğru

et al. 2015

IJET

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Abstract-Providing critical information about daily life, weather forecasting has important role for human being. Especially, temperature forecasting is rather important because it affects not only people but also other atmospheric parameters. given to describe models" forecasting performance and the error. The results show that ANFIS exhibited best forecasting performance on weather forecasting compared to ANN and MRA.

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“…Prediction methods using artificial neural networks (ANN) [8], which are machine learning methods, are widely applied in many different fields [9][10][11][12][13][14], such as the prediction of a building's energy consumption or prediction of the weather, as ANNs do not require a process to simulate a complex system for accurate prediction [9][10][11]. If learning is done properly based on the correlation between target data and input data, the ANN has a high level of prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermal Environment in a Large Space Using Artificial Neural Network

et al. 2018

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Abstract:Since the thermal environment of large space buildings such as stadiums can vary depending on the location of the stands, it is important to divide them into different zones and evaluate their thermal environment separately. The thermal environment can be evaluated using physical values measured with the sensors, but the occupant density of the stadium stands is high, which limits the locations available to install the sensors. As a method to resolve the limitations of installing the sensors, we propose a method to predict the thermal environment of each zone in a large space. We set six key thermal factors affecting the thermal environment in a large space to be predicted factors (indoor air temperature, mean radiant temperature, and clothing) and the fixed factors (air velocity, metabolic rate, and relative humidity). Using artificial neural network (ANN) models and the outdoor air temperature and the surface temperature of the interior walls around the stands as input data, we developed a method to predict the three thermal factors. Learning and verification datasets were established using STAR CCM+ (2016.10, Siemens PLM software, Plano, TX, USA). An analysis of each model's prediction results showed that the prediction accuracy increased with the number of learning data points. The thermal environment evaluation process developed in this study can be used to control heating, ventilation, and air conditioning (HVAC) facilities in each zone in a large space building with sufficient learning by ANN models at the building testing or the evaluation stage.

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Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey

Cited by 103 publications

References 16 publications

Modelling of Decision Making in the Production of Stator Core Using a GA-ANN Approach

Modelling of Decision Making in the Production of Stator Core Using a GA-ANN Approach

Application of Soft Computing Models to Daily Average Temperature Analysis

Prediction of Thermal Environment in a Large Space Using Artificial Neural Network

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