ANN and ANFIS Approaches to Calculate the Heating and Cooling Degree Day Values: The Case of Provinces in Turkey

Işik, Erdem; İnallı, Mustafa; Çelik, Erkan

doi:10.1007/s13369-019-03852-4

Cited by 17 publications

(4 citation statements)

References 28 publications

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“…Their results showed that there was a decrease of about 6-7% in primary energy and 6-8 % in CO2 emission in the optimum versions. Erdem et al [17] calculated the values of heating degree days (HDD) and cooling degree days (CDD) for Turkey by using estimated temperature values. They applied an artificial neural network and an adaptive networkbased fuzzy inference system for the estimation of temperature values.…”

Section: Introductionmentioning

confidence: 99%

Optimization of insulation thickness of walls and roofs using energy, exergy, economic and environmental (4E) analyses

Uçar

2024

J. Mech. Eng. Sci.

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Buildings play an important role in consumption of energy and carbon dioxide emissions all over the world. The optimum thickness of each insulation material of wall and roof of residential buildings depending on energy, environment, economy and exergy was determined in this study. For this purpose, an optimization model was established based on four different criteria: energy, environment, economics, and exergy. A function was defined containing these four criteria. It has been seen from the results that the optimum insulation thickness of the wall and roof depends on the weight coefficients of the energy, environment, economic and exergy parameters and insulation material types. The results of the economic analysis indicate that the optimum insulation thickness of wall varies from 1.01 to 7.7 cm and the optimum thicknesses of roof varies from 3.25 to 6.7 cm for XPS, EPS and Glasswool insulation materials. According to the results of the enviromental analysis, the optimum thicknesses of wall for different insulation materials are 6.5, 8.6, 9.4, and 9.55 cm and optimum insulation thicknesses of roof are 7.55, 8.1 and 8.2 cm, respectively. The effect of economic and energy parameters on the optimum thickness of the wall and roof for the three insulation materials was investigated using the sensitivity analysis method. It was seen from the results that impacts of interest rate, inflation rate, electricity cost, fuel cost, insulation material cost, heating and cooling degree-days on the optimum insulation thickness of wall and roof and 4EF optimization function were different.

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Section: Introductionmentioning

confidence: 99%

Optimization of insulation thickness of walls and roofs using energy, exergy, economic and environmental (4E) analyses

Uçar

2024

J. Mech. Eng. Sci.

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“…In the study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) were used for the estimation of temperature values. [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction Thermal Conductivity of The Novel Developed Light Weight Concrete with Artificial Neural Networks

Koçyiğit

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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The dependence on energy is increasing in a growing population and a rapidly developing global world. Around 40% of the energy consumed is consumed in buildings. Building heating and cooling have boosted energy consumption and costs dramatically. As a consequence, in order to boost energy efficiency in buildings, it becomes inevitable to develop new construction materials with thermal insulation properties. Vermiculite, waste basalt powder, molten tragacanth, and cement-reinforced samples were produced for this purpose. Mechanical and thermal conductivity tests were performed on 48 samples produced at various rates. The findings of the experimentally measured thermal conductivity were modelled and compared with the outputs of the created artificial neural network. The Matlab software was used for modelling. The mechanical properties acquired experimentally using the Artificial Neural Networks (ANN) approach were used as an input, and the correlation of the samples with thermal conductivity was investigated. The findings obtained were consistent with one another, and the thermal conductivity values were predicted with an error ranging between 7.6701% and 0.0091%, and the ANN yielded successful results at a rate of 99%.

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“…ANN techniques are developed by applying hidden layers of neurons between the input and output layers to obtain more accurate results in learning process. Because of its high learning capability, it can be used in various application areas like classification [1], prediction [2] [3], recognition in general (including pattern, handwriting recognition) [4] and so on. ANN with more than three hidden layers between input and output layers is called Deep Neural Networks (DNN).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Derived Features on Art Genre Classification with Machine Learning

Abidin

2021

Sakarya University Journal of Science

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Classification of the artwork according to their genres is being done for years. Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. In this study, two different machine learning algorithms are used on an artwork dataset for genre classification. The primary purpose of this study is to show that the derived features about the artwork have a remarkable effect on correct genre classification. These features are derived from the metadata of the dataset. This metadata contains information about the nationalities and the period that the artist lived. Image filters are also applied to the images but the results show that applying only image filters on the dataset used in the study did not perform well. Instead, adding derived features extracted from the metadata increased the classification performances dramatically.

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ANN and ANFIS Approaches to Calculate the Heating and Cooling Degree Day Values: The Case of Provinces in Turkey

Cited by 17 publications

References 28 publications

Optimization of insulation thickness of walls and roofs using energy, exergy, economic and environmental (4E) analyses

Optimization of insulation thickness of walls and roofs using energy, exergy, economic and environmental (4E) analyses

Prediction Thermal Conductivity of The Novel Developed Light Weight Concrete with Artificial Neural Networks

The Effect of Derived Features on Art Genre Classification with Machine Learning

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