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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 250-265 doi: 10.1007/s11709-021-0785-x

Abstract: Regression analysis was then applied to determine the relationship between SMS, flow rate, andFinally, two prediction models of density were obtained using genetic programming (GP) and group methodof data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluatethe models.By the use of regression analysis, the mathematical relationships between variables in all facilities

Keywords： pedestrian density     regression analysis     GP model     GMDH model    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: With this study, along with the conventional methods of simple and multiple linear regression models,three machine learning algorithms, random forest, gradient boosting and stacked models, are developedThese models are employed on a relatively large data set from different projects around Turkey coveringlarge database, such as using a train/test split on the data set to avoid overfitting of the developed modelsAccordingly, the models developed in this study demonstrate superior prediction capabilities compared

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 474-485 doi: 10.1631/FITEE.1400295

Abstract: In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificialThe selected PCs are fed into the proposed models for modeling and testing.The evaluation of the performance of the constructed models, using appropriate statistical indices, showsThis assessment demonstrates the importance and advantages posed by hybrid models for the prediction

Keywords： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural    

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Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 2,   Pages 177-187 doi: 10.15302/J-FASE-2017177

Abstract: To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model is

Keywords： inexact two-stage stochastic programming     runoff forecasting     Shiyang River Basin     uncertain multiple linear regression    

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Option-like properties in the distribution of hedge fund returns

Katharina DENK, Ben DJERROUD, Luis SECO, Mohammad SHAKOURIFAR, Rudi ZAGST

Frontiers of Engineering Management 2020, Volume 7, Issue 2,   Pages 275-286 doi: 10.1007/s42524-020-0095-3

Abstract: two different model approaches for the equity long/short strategy, where weighted segmented linear regressionmodels are employed and combined with two-state Markov switching models.

Keywords： hedge funds     hedge fund index     segmented linear regression models     regime-switching models     mimicking portfolios    

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Unified methodology for site-characterization and sampling of highway runoff

Jy S. WU, Craig J. ALLAN,

Frontiers of Environmental Science & Engineering 2010, Volume 4, Issue 1,   Pages 47-58 doi: 10.1007/s11783-010-0003-x

Abstract: Carolina, USA, were used to illustrate the development of site-specific highway-runoff pollutant loading modelsadditional traffic and/or atmospheric component variables are incorporated into the basic hydrologic regression

Keywords： highway runoff     pollutant loads     regression models     non-point source pollution     storm water permit    

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Models of thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture

WEI Wenjian, DING Guoliang, HU Haitao, WANG Kaijian

Frontiers in Energy 2008, Volume 2, Issue 2,   Pages 227-234 doi: 10.1007/s11708-008-0020-7

Abstract: Models of the thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture were providedNew models were developed by modifying the coefficients in existing correlations with multiple regressionThe maximum deviation of the predicted values of these models to the experimental data is within 5%.These models can be used for R410A/POE VG68 to obtain accurate and reliable thermodynamic and transport

Keywords： multiple regression     foundation     thermodynamic     influence     air-conditioning    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based modelsTwo artificial-intelligence-based models including artificial neural networks and support vector machinesThe suggested models predicted the unconfined compressive strength of soils accurately and can be introducedas reliable predictive models in geotechnical engineering.of the proposed models.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Optimal Antibody Purification Strategies Using Data-Driven Models Article

Songsong Liu, Lazaros G. Papageorgiou

Engineering 2019, Volume 5, Issue 6,   Pages 1077-1092 doi: 10.1016/j.eng.2019.10.011

Abstract: Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity,The piecewise linear regression modeling method is adapted due to its simplicity and better predictionTwo alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the totalcost of goods per gram of the antibody purification process, incorporating the data-driven models.These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization

Keywords： optimization     Antigen-binding fragment     Mixed-integer programming     Data-driven model     Piecewise linear regression    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forthe estimation of DRSM models.compared to the models in the literature.Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: Finite element (FE) models are prepared with various design variables in a double-layer soil system,The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificialneural network (ANN) modeling, and some prediction models are proposed.ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factorsThe factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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A review on different theoretical models of electrocaloric effect for refrigeration

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 478-503 doi: 10.1007/s11708-023-0884-6

Abstract: This paper reviews the electrocaloric effect of ferroelectric materials based on different theoretical models

Keywords： electrocaloric effect     effective Hamiltonian     phase-field modeling     different theoretical models    

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An innovative model for predicting the displacement and rotation of column-tree moment connection under fire

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 194-212 doi: 10.1007/s11709-020-0688-2

Abstract: related variables for each output are imported for the training and testing of different surrogate modelsThese surrogate models include a multiple linear regression (MLR), multiple Ln equation regression (MLnER

Keywords： column-tree moment connection     Finite element model     parametric study     fire     regression models     gene expression    

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Title Author Date Type Operation

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Journal Article

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

Journal Article

Option-like properties in the distribution of hedge fund returns

Katharina DENK, Ben DJERROUD, Luis SECO, Mohammad SHAKOURIFAR, Rudi ZAGST

Journal Article

Unified methodology for site-characterization and sampling of highway runoff

Jy S. WU, Craig J. ALLAN,

Journal Article

Models of thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture

WEI Wenjian, DING Guoliang, HU Haitao, WANG Kaijian

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Optimal Antibody Purification Strategies Using Data-Driven Models

Songsong Liu, Lazaros G. Papageorgiou

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

A review on different theoretical models of electrocaloric effect for refrigeration

Journal Article

An innovative model for predicting the displacement and rotation of column-tree moment connection under fire

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

Journal Article