Prediction of temperature decreasing on a green roof by using artificial neural network

Erdemir, Doğan; Ayata, Tahir

doi:10.1016/j.applthermaleng.2016.10.145

Cited by 32 publications

(18 citation statements)

References 22 publications

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“…The only exception is the coefficient of determination, which is extraordinarily high when targeting the temperature (R 2 = 0.99). This is also in line with previous studies (e.g., [75,77]) and it is further addressed in the discussion section.…”

Section: Resultssupporting

confidence: 92%

“…For example, Gobakis et al [24] and Papantoniou and Kolokotsa [76] used the date in conjunction with air temperature and global solar radiation. Similarly, Heijden et al [35] and Erdemir and Ayata [77] used the hour of the day together with other meteorological parameters. Table 1 summarizes these and other ANN studies that focused on outdoor urban temperatures and their modelling characteristics, such as the length of their datasets.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

et al. 2021

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In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

et al. 2021

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“…A total of 128 data obtained from experimental study were used to set an ANN model. The number of different hidden neurons (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and two different training algorithms (LM and SCG) were used to achieve the optimal network with the best performance. As a result, the optimal network was obtained in 16 hidden neurons and the LM algorithm.…”

Section: Zaključakmentioning

confidence: 99%

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

2019

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In this study, water absorption and thickness swelling values of medium density fiberboard (MDF) were modelled by artificial neural networks (ANN). MDF panels were produced with different rates of paraffin (0.0-control, 0.5, 1 and 1.5 %) at different press temperatures (170 and 190 °C). After conditioning of MDF, water absorption (WA) and thickness swelling (TS) of samples were carried out at specific intervals within 24 hours. Then, the data obtained from these experiment were modelled using ANN. Paraffin addition rate, press temperature and immersion time in water were used as the input parameters, while WA and TS values of MDF were used as the output parameters. After training of ANN, it was found that correlation coefficients (R) were close to 1 for training, validation, test and all data set. Mean absolute percentage error (MAPE) and mean square error (MSE) were determined as 2.94 % and 0.57, respectively, for all data sets. As a result of this study, the use of proposed ANN model may be recommended to predict the water absorption and thickness swelling of panels instead of complex and time-consuming studies such as empirical formulas.

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“…As an intelligent algorithm, ANN has been developed rapidly in recent years and applied in various studies [7]. This approach has also been applied in the heat transfer field, such as the prediction of temperature field [8]. The ANN's feasibility of predicting the thermal and flow variables due to natural convection in a complicated domain has been verified in several papers.…”

Section: Introductionmentioning

confidence: 99%

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Zhou

Liu

et al. 2019

THERM SCI

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Natural convection in an enclosure is a classical problem in heat transfer field. In this study, natural convection induced by the heat source in the enclosure is studied with two analysis methods, i. e. CFD and artificial neural networks (ANN). The heat transfer in the enclosure is an unsteady process. During this process, the temperature fields in the enclosure are changing with time. The vertical temperature field of y = 0 at one moment is picked up for investigation. Firstly, FLUENT software which is a simulation program of CFD is adopted to simulate the temperature fields under different computation conditions. Then part of the simulation condition's temperature data is picked for training an ANN model and the rest of data is used for validating the ANN model. It has been found from the comparison between the CFD simulation and ANN prediction that the two results have a good agreement with each other. In the comparison, the max relative errors are around 12%, mean relative errors are around 0.3%, mean square errors are around 0.6%, values of absolute fraction of variance are all not less than 0.99. The results demonstrated that the ANN prediction have enough accuracy.

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Prediction of temperature decreasing on a green roof by using artificial neural network

Cited by 32 publications

References 22 publications

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

Application of artificial neural network to predict the effect of paraffin addition on water absorption and thickness swelling of MDF

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

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