Artificial Intelligence-Based Prediction Models for Energy Performance of Residential Buildings

Namlı, Ersin; Erdal, Hami̇t; Erdal, Halil İbrahim

doi:10.1007/978-3-319-95888-0_12

Cited by 11 publications

(27 citation statements)

References 15 publications

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“…We began search for a "better" tool that provided a way to compare, for instance, a metric. We extensively researched this subject and found several methods for measure/comparing models' performance, such as: Accuracy, confusion matrix, and several associate measures such as sensitivity, specificity, precision, recall or f-measure (very useful when facing classification problems with imbalanced classes), MSE-mean square error, ROC-Receiver Operating Characteristic Curve, AUC-area under the roc curve, RMSE-root mean squared error, MAE-mean absolute error, R 2 -R Squared, Log-loss, Cohen's kappa, Sum squared error, Silhouette coefficient, Euclidean distance, or Manhattan distance [30][31][32][33].…”

Section: Business Understandingmentioning

confidence: 99%

Child’s Target Height Prediction Evolution

2019

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This study is a contribution for the improvement of healthcare in children and in society generally. This study aims to predict children's height when they become adults, also known as "target height", to allow for a better growth assessment and more personalized healthcare. The existing literature describes some existing prediction methods, based on longitudinal population studies and statistical techniques, which with few information resources, are able to produce acceptable results. The challenge of this study is in using a new approach based on machine learning to forecast the target height for children and (eventually) improve the existing height prediction accuracy. The goals of the study were achieved. The extreme gradient boosting regression (XGB) and light gradient boosting machine regression (LightGBM) algorithms achieved considerably better results on the height prediction. The developed model can be usefully applied by pediatricians and other clinical professionals in growth assessment.

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Section: Business Understandingmentioning

confidence: 99%

Child’s Target Height Prediction Evolution

2019

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“…40 M5 algorithm-based model regression trees are known to work on divide-and-conquer policy. It is relatively smaller than other trees and also contains the fewer variables Namlı et al 56 reported M5P to have less variables than other tree-based MLAs. Zhan et al 58 observed the M5P algorithm to develop multiple linear regression models at the tree leaves.…”

Section: Data Augmentationmentioning

confidence: 95%

“…Brief descriptions of the employed algorithms are discussed below. MLP is a commonly utilized back-propagationbased ANN tool, 38,56 and is used in various applications. 37,39 SVM is a statistics-based MLA developed by Vapnik.…”

Section: Data Augmentationmentioning

confidence: 99%

“…65 A lazy learner-based nonparametric statistical method, known to represent a non-linear problem using piecewise simplification is called the locally weighted learning (LWL) MLA. 56,66,67 Schaal and Atkeson 66 observed LWL to be accurate, fast in convergence and simple to implement.…”

Section: Data Augmentationmentioning

confidence: 99%

“…Moreover, the employed piecewise simplification of a non-linear problem may have failed to capture the complexity associated with the present problem. 56,66,67…”

Section: Performance Comparison Of the Different Employed Mlasmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Das

Pratihar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

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Study of micro-porosity in electron beam butt welding

Das

Dinda

Das

et al. 2022

Int J Adv Manuf Technol

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As complete elimination of porosity from the weld is very difficult, the next option available is to minimize this weld porosity, which is crucial for the safe performance of the welded components. However, this investigation through experiments alone is very tedious and time consuming. Additionally, very limited models are available in the literature for accurate prediction of different porosity attributes. The present study, thus, addressees both the experimental as well as modelling aspect on the study of micro-porosity during Electron Beam Welding (EBW) of SS304 plates. Welding parameters are reported to have significant influence on the micro-porosity. Hence, the influences of these parameters on micro-porosity attributes, namely pores number, average diameter, and sphericity are extensively studied experimentally employing optical microscopy (OM), scanning electron microscopy (SEM), X-ray computed tomography (XCT), and Raman spectroscopy. This is followed by an elaborate modelling through seven popular and wellrecognized Machine Learning Algorithms (MLAs) namely, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), M5P model trees regression, Reduced Error Pruning Tree (REPTree), Random Forest (RF), Instance-based k-nearest neighbor algorithm (IBk) and Locally Weighted Learning (LWL). These different techniques enhance the chance of obtaining the better predictions of the said micro-porosity attributes by overcoming the effect of data-dependence and other limitations of individual MLAs. The different model-predicted micro-porosity data are also validated through experimental data. Statistical tests and Monte-Carlo reliability analysis are additionally utilized to evaluate the performances of the employed algorithms. IBk and MLP are overall found to perform well.

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Artificial Intelligence-Based Prediction Models for Energy Performance of Residential Buildings

Cited by 11 publications

References 15 publications

Child’s Target Height Prediction Evolution

Child’s Target Height Prediction Evolution

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Study of micro-porosity in electron beam butt welding

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