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description Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Eiman Tamah Al-Shammari;
Eiman Tamah Al-Shammari
Eiman Tamah Al-Shammari in OpenAIREAfram Keivani;
Shahaboddin Shamshirband; Ali Mostafaeipour; +3 AuthorsAfram Keivani
Afram Keivani in OpenAIREEiman Tamah Al-Shammari;
Eiman Tamah Al-Shammari
Eiman Tamah Al-Shammari in OpenAIREAfram Keivani;
Shahaboddin Shamshirband; Ali Mostafaeipour;Afram Keivani
Afram Keivani in OpenAIREPor Lip Yee;
Por Lip Yee
Por Lip Yee in OpenAIREDalibor Petković;
Sudheer Ch;Dalibor Petković
Dalibor Petković in OpenAIREDistrict heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.079&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.079&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Eiman Tamah Al-Shammari;
Eiman Tamah Al-Shammari
Eiman Tamah Al-Shammari in OpenAIREAfram Keivani;
Shahaboddin Shamshirband; Ali Mostafaeipour; +3 AuthorsAfram Keivani
Afram Keivani in OpenAIREEiman Tamah Al-Shammari;
Eiman Tamah Al-Shammari
Eiman Tamah Al-Shammari in OpenAIREAfram Keivani;
Shahaboddin Shamshirband; Ali Mostafaeipour;Afram Keivani
Afram Keivani in OpenAIREPor Lip Yee;
Por Lip Yee
Por Lip Yee in OpenAIREDalibor Petković;
Sudheer Ch;Dalibor Petković
Dalibor Petković in OpenAIREDistrict heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.079&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu110 citations 110 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.079&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Sareh Naji;
Sareh Naji
Sareh Naji in OpenAIREShahaboddin Shamshirband;
Hossein Basser;Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREAfram Keivani;
+3 AuthorsAfram Keivani
Afram Keivani in OpenAIRESareh Naji;
Sareh Naji
Sareh Naji in OpenAIREShahaboddin Shamshirband;
Hossein Basser;Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREU. Johnson Alengaram;
U. Johnson Alengaram
U. Johnson Alengaram in OpenAIREMohd Zamin Jumaat;
Dalibor Petković;Mohd Zamin Jumaat
Mohd Zamin Jumaat in OpenAIREAbstract The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.
Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.09.062&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu55 citations 55 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.09.062&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Sareh Naji;
Sareh Naji
Sareh Naji in OpenAIREShahaboddin Shamshirband;
Hossein Basser;Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREAfram Keivani;
+3 AuthorsAfram Keivani
Afram Keivani in OpenAIRESareh Naji;
Sareh Naji
Sareh Naji in OpenAIREShahaboddin Shamshirband;
Hossein Basser;Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREU. Johnson Alengaram;
U. Johnson Alengaram
U. Johnson Alengaram in OpenAIREMohd Zamin Jumaat;
Dalibor Petković;Mohd Zamin Jumaat
Mohd Zamin Jumaat in OpenAIREAbstract The huge demand for energy and construction materials has become an issue of great concern recently. The energy usage of buildings accounts for a large percentage of the total primary energy consumption. The total energy requirement of buildings is influenced by various factors, including environmental and climatic conditions, building envelope materials, insulation, etc. In this respect, estimating the operational energy of buildings is potentially helpful for architects and engineers in the early design and construction stages. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate the energy consumption of buildings according to the main building envelope parameters, namely material thickness and insulation K-value. Up to 180 simulations using different material thickness values and insulation properties are carried out in EnergyPlus software in order to use for estimation. This soft computing methodology is implemented with Matlab/Simulink and the performance is investigated.
Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.09.062&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu55 citations 55 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.09.062&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors: Dalibor Petković; Malrey Lee;Afram Keivani;
Kasra Mohammadi; +2 AuthorsAfram Keivani
Afram Keivani in OpenAIREDalibor Petković; Malrey Lee;Afram Keivani;
Kasra Mohammadi; Siti Hafizah Abd Hamid; Shahaboddin Shamshirband;Afram Keivani
Afram Keivani in OpenAIREAbstract The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.
Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.12.269&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.12.269&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors: Dalibor Petković; Malrey Lee;Afram Keivani;
Kasra Mohammadi; +2 AuthorsAfram Keivani
Afram Keivani in OpenAIREDalibor Petković; Malrey Lee;Afram Keivani;
Kasra Mohammadi; Siti Hafizah Abd Hamid; Shahaboddin Shamshirband;Afram Keivani
Afram Keivani in OpenAIREAbstract The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.
Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.12.269&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu14 citations 14 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Renewable and Sustai... arrow_drop_down Renewable and Sustainable Energy ReviewsArticle . 2016 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.rser.2015.12.269&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG Authors:Milad Baghalzadeh Shishehgarkhaneh;
Milad Baghalzadeh Shishehgarkhaneh
Milad Baghalzadeh Shishehgarkhaneh in OpenAIRESina Fard Moradinia;
Sina Fard Moradinia
Sina Fard Moradinia in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREMahdi Azizi;
Mahdi Azizi
Mahdi Azizi in OpenAIREIn recent years, dam construction has become more complex, requiring an effective project management method. Building Information Modeling (BIM) affects how construction projects are planned, designed, executed, and operated. Therefore, reducing execution time, cost, and risk and increasing quality are the primary goals of organizations. In this paper, first, the time and cost of the project were obtained via the BIM process. Subsequently, optimization between the components of the survival pyramid (time, cost, quality, and risk) in construction projects was completed in a case study of the Ghocham storage dam in five different modes, including contractor’s offers, BIM, actual, and two other modes based on the expert’s opinions. For this aim, five different meta-heuristic optimization algorithms were utilized, including two classical algorithms (Genetic and Simulated Annealing) and three novel algorithms (Black Widow Optimization, Battle Royale Optimization, and Black Hole Mechanics Optimization). In four cases, once each element of the survival pyramid was optimized separately, all four cases were traded off simultaneously. Moreover, the results were obtained from all the mentioned algorithms in five scenarios based on the number of function evaluation (Nfe), Standard Deviation (SD), Computation Time (CT), and Best Cost (BC). MATLAB software completed the coding related to the objective functions and optimization algorithms. The results indicated the appropriate performance of GA and BHMO algorithms in some scenarios. However, only the GAs should be considered effective algorithms in a dam construction projects’ time–cost–quality–risk (TCQR) tradeoff.
Smart Cities arrow_drop_down Smart CitiesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2624-6511/5/4/74/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/smartcities5040074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Smart Cities arrow_drop_down Smart CitiesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2624-6511/5/4/74/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/smartcities5040074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022Publisher:MDPI AG Authors:Milad Baghalzadeh Shishehgarkhaneh;
Milad Baghalzadeh Shishehgarkhaneh
Milad Baghalzadeh Shishehgarkhaneh in OpenAIRESina Fard Moradinia;
Sina Fard Moradinia
Sina Fard Moradinia in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREMahdi Azizi;
Mahdi Azizi
Mahdi Azizi in OpenAIREIn recent years, dam construction has become more complex, requiring an effective project management method. Building Information Modeling (BIM) affects how construction projects are planned, designed, executed, and operated. Therefore, reducing execution time, cost, and risk and increasing quality are the primary goals of organizations. In this paper, first, the time and cost of the project were obtained via the BIM process. Subsequently, optimization between the components of the survival pyramid (time, cost, quality, and risk) in construction projects was completed in a case study of the Ghocham storage dam in five different modes, including contractor’s offers, BIM, actual, and two other modes based on the expert’s opinions. For this aim, five different meta-heuristic optimization algorithms were utilized, including two classical algorithms (Genetic and Simulated Annealing) and three novel algorithms (Black Widow Optimization, Battle Royale Optimization, and Black Hole Mechanics Optimization). In four cases, once each element of the survival pyramid was optimized separately, all four cases were traded off simultaneously. Moreover, the results were obtained from all the mentioned algorithms in five scenarios based on the number of function evaluation (Nfe), Standard Deviation (SD), Computation Time (CT), and Best Cost (BC). MATLAB software completed the coding related to the objective functions and optimization algorithms. The results indicated the appropriate performance of GA and BHMO algorithms in some scenarios. However, only the GAs should be considered effective algorithms in a dam construction projects’ time–cost–quality–risk (TCQR) tradeoff.
Smart Cities arrow_drop_down Smart CitiesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2624-6511/5/4/74/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/smartcities5040074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Smart Cities arrow_drop_down Smart CitiesOther literature type . 2022License: CC BYFull-Text: http://www.mdpi.com/2624-6511/5/4/74/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/smartcities5040074&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Sareh Naji;
Sareh Naji
Sareh Naji in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREShahaboddin Shamshirband;
Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREU. Johnson Alengaram;
+3 AuthorsU. Johnson Alengaram
U. Johnson Alengaram in OpenAIRESareh Naji;
Sareh Naji
Sareh Naji in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREShahaboddin Shamshirband;
Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREU. Johnson Alengaram;
U. Johnson Alengaram
U. Johnson Alengaram in OpenAIREMohd Zamin Jumaat;
Zulkefli Mansor; Malrey Lee;Mohd Zamin Jumaat
Mohd Zamin Jumaat in OpenAIREAbstract The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.037&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu173 citations 173 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors:Sareh Naji;
Sareh Naji
Sareh Naji in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREShahaboddin Shamshirband;
Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREU. Johnson Alengaram;
+3 AuthorsU. Johnson Alengaram
U. Johnson Alengaram in OpenAIRESareh Naji;
Sareh Naji
Sareh Naji in OpenAIREAfram Keivani;
Afram Keivani
Afram Keivani in OpenAIREShahaboddin Shamshirband;
Shahaboddin Shamshirband
Shahaboddin Shamshirband in OpenAIREU. Johnson Alengaram;
U. Johnson Alengaram
U. Johnson Alengaram in OpenAIREMohd Zamin Jumaat;
Zulkefli Mansor; Malrey Lee;Mohd Zamin Jumaat
Mohd Zamin Jumaat in OpenAIREAbstract The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.037&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu173 citations 173 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2015.11.037&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu