Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors
Summary This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function...
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| Vydané v: | International journal of energy research Ročník 44; číslo 7; s. 5675 - 5695 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Chichester, UK
John Wiley & Sons, Inc
10.06.2020
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| Predmet: | |
| ISSN: | 0363-907X, 1099-114X |
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| Abstract | Summary
This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input–output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R2 ranges (0.9997‐0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models.
Reliable predictive models of the exergetic performance of a BIPVT collector in Iran were developed in this study. The Performance Evaluation Criteria (PEC) was used to assess the performance of the BIPVT collector. The most reliable predictions of PEC were obtained from the Random Forest (RF) method. |
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| AbstractList | Summary
This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input–output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R2 ranges (0.9997‐0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models.
Reliable predictive models of the exergetic performance of a BIPVT collector in Iran were developed in this study. The Performance Evaluation Criteria (PEC) was used to assess the performance of the BIPVT collector. The most reliable predictions of PEC were obtained from the Random Forest (RF) method. This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated photovoltaic‐thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input–output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R2 ranges (0.9997‐0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models. |
| Author | Sopian, Kamaruzzaman Al‐Waeli, Ali H. A. Shahsavar, Amin Moayedi, Hossein Chelvanathan, Puvaneswaran |
| Author_xml | – sequence: 1 givenname: Amin orcidid: 0000-0003-0493-898X surname: Shahsavar fullname: Shahsavar, Amin organization: Kermanshah University of Technology – sequence: 2 givenname: Hossein orcidid: 0000-0002-5625-1437 surname: Moayedi fullname: Moayedi, Hossein organization: Ton Duc Thang University – sequence: 3 givenname: Ali H. A. orcidid: 0000-0001-5064-9180 surname: Al‐Waeli fullname: Al‐Waeli, Ali H. A. email: ali9alwaeli@gmail.com organization: Universiti Kebangsaan Malaysia (UKM) – sequence: 4 givenname: Kamaruzzaman orcidid: 0000-0002-4675-3927 surname: Sopian fullname: Sopian, Kamaruzzaman organization: Universiti Kebangsaan Malaysia (UKM) – sequence: 5 givenname: Puvaneswaran surname: Chelvanathan fullname: Chelvanathan, Puvaneswaran organization: Universiti Kebangsaan Malaysia (UKM) |
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| Cites_doi | 10.1007/BF02478259 10.1016/j.applthermaleng.2018.09.101 10.1016/j.enbuild.2015.11.045 10.1016/j.csite.2019.100547 10.1002/er.4807 10.1016/j.renene.2015.07.014 10.1016/j.jag.2009.06.002 10.1016/0893-6080(89)90020-8 10.1016/j.solener.2010.07.010 10.1016/j.renene.2017.06.085 10.1016/j.camwa.2016.04.048 10.1016/j.renene.2017.01.062 10.1016/j.solener.2017.09.056 10.1016/j.apenergy.2017.09.039 10.1016/j.solener.2006.08.002 10.1115/1.4005250 10.1002/9781118671603 10.1016/j.solener.2012.09.001 10.1109/34.58871 10.1016/j.energy.2015.09.078 10.1126/science.247.4945.978 10.1016/j.energy.2015.05.017 10.2307/2685209 10.1016/j.egypro.2015.07.819 10.1016/j.enconman.2018.02.039 10.1016/j.apenergy.2018.07.055 10.1023/A:1010933404324 10.1016/j.applthermaleng.2016.12.104 10.1016/j.asoc.2018.02.027 10.1002/er.4855 10.1016/j.enconman.2018.01.006 10.1016/j.enconman.2018.07.034 10.1016/j.enconman.2018.07.057 10.1016/j.solener.2019.03.016 10.1016/j.solener.2017.06.006 10.1016/j.isprsjprs.2010.11.001 10.1007/978-3-540-68017-8_131 10.1115/1.4038050 10.1016/j.enbuild.2014.06.009 10.1109/TPAMI.2015.2414422 10.1016/j.energy.2018.08.044 10.1016/j.enbuild.2011.05.003 10.1016/j.ijheatmasstransfer.2017.11.011 10.1016/j.solener.2018.07.051 10.1007/s00365-004-0585-2 10.1016/j.compstruct.2010.08.018 10.1016/j.enconman.2018.05.011 |
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| References | 2018; 163 2015; 37 2018; 160 2013; 3 1990; 12 2018; 162 2013; 1 2013; 2 2018; 128 2018; 168 2015; 74 2019; 16 2005; 21 2018; 209 2016; 72 2017; 153 2001; 45 2017; 158 2017; 115 2002; 2001 2009; 11 2012; 134 2018; 173 2018; 172 2015; 87 1987 2016; 111 2016; 85 2011; 66 1992; 46 1988 1989; 2 1943; 5 2018; 140 1990; 247 2015; 93 2018; 228 2018; 146 1998 2008 2007 2006 2018; 66 2019; 183 2010; 84 2006; 81 2017; 109 2014; 81 2012; 1 2019; 43 2018; 118 2011; 93 2011; 43 2013 2018; 55 2012; 86 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_41_1 e_1_2_8_60_1 e_1_2_8_17_1 e_1_2_8_19_1 Boztosun I (e_1_2_8_46_1) 2002; 2001 Raviya KH (e_1_2_8_54_1) 2013; 2 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_59_1 e_1_2_8_15_1 Broomhead DS (e_1_2_8_38_1) 1988 Cherkassky VS (e_1_2_8_51_1) 2006 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 Ghosh S (e_1_2_8_57_1) 2012; 1 e_1_2_8_30_1 e_1_2_8_29_1 Vapnik V (e_1_2_8_50_1) 2013 e_1_2_8_25_1 Patil S (e_1_2_8_52_1) 2013; 3 e_1_2_8_27_1 e_1_2_8_48_1 Vijayarani S (e_1_2_8_58_1) 2013; 1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 Soleymani F (e_1_2_8_45_1) 2018; 55 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 Makridakis S (e_1_2_8_33_1) 2008 e_1_2_8_12_1 |
| References_xml | – volume: 5 start-page: 115 year: 1943 end-page: 133 article-title: A logical calculus of the ideas immanent in nervous activity publication-title: Bull Math Biophys – volume: 118 start-page: 734 year: 2018 end-page: 745 article-title: Multiquadric RBF‐FD method for the convection‐dominated diffusion problems base on Shishkin nodes publication-title: Int J Heat Mass Transf – volume: 168 start-page: 371 year: 2018 end-page: 381 article-title: Experimental investigation on Peltier based hybrid PV/T active solar still for enhancing the overall performance publication-title: Energy Convers Manag – volume: 93 start-page: 611 year: 2011 end-page: 615 article-title: Thin plate spline radial basis functions for vibration analysis of clamped laminated composite plates publication-title: Compos Struct – volume: 37 start-page: 2464 year: 2015 end-page: 2477 article-title: Kernel methods on Riemannian manifolds with Gaussian RBF kernels publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 85 start-page: 1052 year: 2016 end-page: 1067 article-title: Bi‐fluid photovoltaic/thermal (PV/T) solar collector: experimental validation of a 2‐D theoretical model publication-title: Renew Energy – volume: 46 start-page: 175 year: 1992 end-page: 185 article-title: An introduction to kernel and nearest‐neighbor nonparametric regression publication-title: Am Stat – volume: 162 start-page: 682 year: 2018 end-page: 696 article-title: Performance assessment of an innovative exhaust air energy recovery system based on the PV/T‐assisted thermal wheel publication-title: Energy – volume: 1 start-page: 7 year: 2012 article-title: A tutorial review on text mining algorithms publication-title: Int J Adv Res Comput Commun Eng – volume: 21 start-page: 293 year: 2005 end-page: 317 article-title: Multivariate interpolation by polynomials and radial basis functions publication-title: Constr Approx – volume: 173 start-page: 1002 year: 2018 end-page: 1010 article-title: Effect of glass cover and working fluid on the performance of photovoltaic thermal (PVT) system: an experimental study publication-title: Sol Energy – volume: 160 start-page: 93 year: 2018 end-page: 108 article-title: Optimization and parametric analysis of a nanofluid based photovoltaic thermal system: 3D numerical model with experimental validation publication-title: Energy Convers Manag – year: 1998 – volume: 2001 start-page: 267 year: 2002 end-page: 282 article-title: Thin‐plate spline radial basis function scheme for advection‐diffusion problems publication-title: Electron J Bound Elem – volume: 2 start-page: 359 year: 1989 end-page: 366 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Netw – volume: 158 start-page: 380 year: 2017 end-page: 395 article-title: Energy and exergy analysis and multi‐objective optimization of an air based building integrated photovoltaic/thermal (BIPV/T) system publication-title: Sol Energy – volume: 72 start-page: 178 year: 2016 end-page: 193 article-title: A stable method for the evaluation of Gaussian radial basis function solutions of interpolation and collocation problems publication-title: Comput Math Appl – year: 2008 – volume: 43 start-page: 8100 year: 2019 end-page: 8117 article-title: Mathematical and neural network models for predicting the electrical performance of a PV/T system publication-title: Int J Energy Res – volume: 3 start-page: 266 year: 2013 end-page: 275 article-title: Identification of growth rate of plant based on leaf features using digital image processing techniques publication-title: Int J Emerging Technol Adv Eng – volume: 172 start-page: 343 year: 2018 end-page: 356 article-title: Numerical simulation of a concentrating photovoltaic‐thermal solar system combined with thermoelectric modules by coupling finite volume and Monte Carlo ray‐tracing methods publication-title: Energy Convers Manag – volume: 134 year: 2012 article-title: Energy and exergy analysis of a photovoltaic‐thermal collector with natural air flow publication-title: J Sol Energy Eng – volume: 12 start-page: 993 year: 1990 end-page: 1001 article-title: Neural network ensembles publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 172 start-page: 595 year: 2018 end-page: 610 article-title: Energy analysis and multi‐objective optimization of a novel exhaust air heat recovery system consisting of an air‐based building integrated photovoltaic/thermal system and a thermal wheel publication-title: Energy Convers Manag – volume: 66 start-page: 247 year: 2011 end-page: 259 article-title: Support vector machines in remote sensing: a review publication-title: ISPRS J Photogramm Remote Sens – volume: 115 start-page: 178 year: 2017 end-page: 187 article-title: Evaluating the environmental parameters affecting the performance of photovoltaic thermal system using nanofluid publication-title: Appl Therm Eng – volume: 1 start-page: 735 year: 2013 end-page: 741 article-title: Comparative analysis of classification function techniques for heart disease prediction publication-title: Int J Innovative Res Comput Comm Eng – volume: 84 start-page: 1938 year: 2010 end-page: 1958 article-title: Experimental investigation and modeling of a direct‐coupled PV/T air collector publication-title: Sol Energy – volume: 209 start-page: 355 year: 2018 end-page: 382 article-title: Assessing active and passive effects of façade building integrated photovoltaics/thermal systems: dynamic modelling and simulation publication-title: Appl Energy – year: 1987 – year: 2007 – volume: 183 start-page: 293 year: 2019 end-page: 305 article-title: The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system publication-title: Sol Energy – volume: 86 start-page: 3378 issue: 11 year: 2012 end-page: 3387 article-title: Estimation of photovoltaic conversion efficiency of a building integrated photovoltaic/thermal (BIPV/T) collector array using an artificial neural network publication-title: Sol Energy – volume: 66 start-page: 208 year: 2018 end-page: 219 article-title: Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods publication-title: Appl Soft Comput – volume: 2 start-page: 19 year: 2013 end-page: 21 article-title: Performance evaluation of different data mining classification algorithm using WEKA publication-title: Indian J Res – volume: 153 start-page: 562 year: 2017 end-page: 573 article-title: Performance of a building integrated photovoltaic/thermal concentrator for facade applications publication-title: Sol Energy – volume: 93 start-page: 908 year: 2015 end-page: 922 article-title: Artificial neural network modeling of a photovoltaic‐thermal evaporator of solar assisted heat pumps publication-title: Energy – volume: 55 start-page: 51 year: 2018 article-title: A multiquadric RBF–FD scheme for simulating the financial HHW equation utilizing exponential integrator publication-title: CAL – volume: 163 start-page: 187 year: 2018 end-page: 195 article-title: Numerical investigation of the effects of a copper foam filled with phase change materials in a water‐cooled photovoltaic/thermal system publication-title: Energy Convers Manag – volume: 45 start-page: 5 year: 2001 end-page: 32 article-title: Random forests publication-title: Mach Learn – volume: 228 start-page: 1531 year: 2018 end-page: 1539 article-title: Experimental evaluation of a prototype hybrid CPV/T system utilizing a nanoparticle fluid absorber at elevated temperatures publication-title: Appl Energy – volume: 43 start-page: 8572 year: 2019 end-page: 8591 article-title: Experimental and deep learning artificial neural network approach for evaluating grid‐connected photovoltaic systems publication-title: Int J Energy Res – volume: 111 start-page: 184 year: 2016 end-page: 194 article-title: Artificial neural network model for forecasting sub‐hourly electricity usage in commercial buildings publication-title: Energ Buildings – year: 1988 – volume: 81 start-page: 498 year: 2006 end-page: 511 article-title: Improved PV/T solar collectors with heat extraction by forced or natural air circulation publication-title: Sol Energy – year: 2006 – volume: 87 start-page: 470 year: 2015 end-page: 480 article-title: Numerical simulation and experimental validation of tri‐functional photovoltaic/thermal solar collector publication-title: Energy – volume: 109 start-page: 168 year: 2017 end-page: 187 article-title: Numerical studies on thermal and electrical performance of a fully wetted absorber PVT collector with PCM as a storage medium publication-title: Renew Energy – volume: 247 start-page: 978 year: 1990 end-page: 982 article-title: Regularization algorithms for learning that are equivalent to multilayer networks publication-title: Science – volume: 43 start-page: 2219 year: 2011 end-page: 2226 article-title: Energy saving in buildings by using the exhaust and ventilation air for cooling of photovoltaic panels publication-title: Energ Buildings – volume: 128 start-page: 541 year: 2018 end-page: 552 article-title: Exergy analysis of a naturally ventilated building integrated photovoltaic/thermal (BIPV/T) system publication-title: Renew Energy – volume: 140 year: 2018 article-title: Scenario‐based multi‐objective optimization of an air‐based building‐integrated photovoltaic/thermal system publication-title: J Sol Energy Eng – volume: 146 start-page: 104 year: 2018 end-page: 122 article-title: Feasibility of a hybrid BIPV/T and thermal wheel system for exhaust air heat recovery: energy and exergy assessment and multi‐objective optimization publication-title: Appl Therm Eng – volume: 74 start-page: 835 year: 2015 end-page: 843 article-title: Analysis of a hybrid solar collector photovoltaic thermal (PVT) publication-title: Energy Procedia – volume: 16 year: 2019 article-title: Novel criteria for assessing PV/T solar energy production publication-title: Case Stud Therm Eng – volume: 11 start-page: 352 year: 2009 end-page: 359 article-title: A kernel functions analysis for support vector machines for land cover classification publication-title: Int J Appl Earth Obs Geo Inf – volume: 81 start-page: 444 year: 2014 end-page: 456 article-title: Multi‐objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application publication-title: Energ Buildings – year: 2013 – ident: e_1_2_8_34_1 doi: 10.1007/BF02478259 – ident: e_1_2_8_18_1 doi: 10.1016/j.applthermaleng.2018.09.101 – ident: e_1_2_8_21_1 doi: 10.1016/j.enbuild.2015.11.045 – ident: e_1_2_8_28_1 doi: 10.1016/j.csite.2019.100547 – ident: e_1_2_8_20_1 doi: 10.1002/er.4807 – ident: e_1_2_8_26_1 doi: 10.1016/j.renene.2015.07.014 – volume: 1 start-page: 735 year: 2013 ident: e_1_2_8_58_1 article-title: Comparative analysis of classification function techniques for heart disease prediction publication-title: Int J Innovative Res Comput Comm Eng – ident: e_1_2_8_48_1 doi: 10.1016/j.jag.2009.06.002 – ident: e_1_2_8_40_1 – ident: e_1_2_8_36_1 doi: 10.1016/0893-6080(89)90020-8 – ident: e_1_2_8_2_1 doi: 10.1016/j.solener.2010.07.010 – ident: e_1_2_8_17_1 doi: 10.1016/j.renene.2017.06.085 – volume-title: Forecasting Methods and Applications year: 2008 ident: e_1_2_8_33_1 – volume: 2 start-page: 19 year: 2013 ident: e_1_2_8_54_1 article-title: Performance evaluation of different data mining classification algorithm using WEKA publication-title: Indian J Res – ident: e_1_2_8_43_1 doi: 10.1016/j.camwa.2016.04.048 – ident: e_1_2_8_23_1 doi: 10.1016/j.renene.2017.01.062 – ident: e_1_2_8_4_1 doi: 10.1016/j.solener.2017.09.056 – ident: e_1_2_8_14_1 doi: 10.1016/j.apenergy.2017.09.039 – ident: e_1_2_8_32_1 doi: 10.1016/j.solener.2006.08.002 – ident: e_1_2_8_6_1 doi: 10.1115/1.4005250 – ident: e_1_2_8_31_1 doi: 10.1002/9781118671603 – ident: e_1_2_8_53_1 – ident: e_1_2_8_27_1 doi: 10.1016/j.solener.2012.09.001 – ident: e_1_2_8_60_1 doi: 10.1109/34.58871 – ident: e_1_2_8_37_1 doi: 10.1016/j.energy.2015.09.078 – ident: e_1_2_8_41_1 doi: 10.1126/science.247.4945.978 – ident: e_1_2_8_24_1 doi: 10.1016/j.energy.2015.05.017 – ident: e_1_2_8_56_1 doi: 10.2307/2685209 – ident: e_1_2_8_22_1 doi: 10.1016/j.egypro.2015.07.819 – ident: e_1_2_8_11_1 doi: 10.1016/j.enconman.2018.02.039 – ident: e_1_2_8_8_1 doi: 10.1016/j.apenergy.2018.07.055 – volume-title: Radial basis functions, multi‐variable functional interpolation and adaptive networks year: 1988 ident: e_1_2_8_38_1 – ident: e_1_2_8_59_1 doi: 10.1023/A:1010933404324 – ident: e_1_2_8_25_1 doi: 10.1016/j.applthermaleng.2016.12.104 – volume: 55 start-page: 51 year: 2018 ident: e_1_2_8_45_1 article-title: A multiquadric RBF–FD scheme for simulating the financial HHW equation utilizing exponential integrator publication-title: CAL – ident: e_1_2_8_35_1 doi: 10.1016/j.asoc.2018.02.027 – ident: e_1_2_8_19_1 doi: 10.1002/er.4855 – ident: e_1_2_8_10_1 doi: 10.1016/j.enconman.2018.01.006 – ident: e_1_2_8_12_1 doi: 10.1016/j.enconman.2018.07.034 – ident: e_1_2_8_16_1 doi: 10.1016/j.enconman.2018.07.057 – ident: e_1_2_8_30_1 doi: 10.1016/j.solener.2019.03.016 – volume: 2001 start-page: 267 year: 2002 ident: e_1_2_8_46_1 article-title: Thin‐plate spline radial basis function scheme for advection‐diffusion problems publication-title: Electron J Bound Elem – ident: e_1_2_8_13_1 doi: 10.1016/j.solener.2017.06.006 – volume: 1 start-page: 7 year: 2012 ident: e_1_2_8_57_1 article-title: A tutorial review on text mining algorithms publication-title: Int J Adv Res Comput Commun Eng – ident: e_1_2_8_49_1 doi: 10.1016/j.isprsjprs.2010.11.001 – ident: e_1_2_8_55_1 doi: 10.1007/978-3-540-68017-8_131 – ident: e_1_2_8_5_1 doi: 10.1115/1.4038050 – ident: e_1_2_8_29_1 doi: 10.1016/j.enbuild.2014.06.009 – ident: e_1_2_8_42_1 doi: 10.1109/TPAMI.2015.2414422 – ident: e_1_2_8_15_1 doi: 10.1016/j.energy.2018.08.044 – volume: 3 start-page: 266 year: 2013 ident: e_1_2_8_52_1 article-title: Identification of growth rate of plant based on leaf features using digital image processing techniques publication-title: Int J Emerging Technol Adv Eng – volume-title: Learning from Data: Concepts, Theory, and Methods year: 2006 ident: e_1_2_8_51_1 – ident: e_1_2_8_3_1 doi: 10.1016/j.enbuild.2011.05.003 – volume-title: The Nature of Statistical Learning Theory year: 2013 ident: e_1_2_8_50_1 – ident: e_1_2_8_44_1 doi: 10.1016/j.ijheatmasstransfer.2017.11.011 – ident: e_1_2_8_9_1 doi: 10.1016/j.solener.2018.07.051 – ident: e_1_2_8_39_1 doi: 10.1007/s00365-004-0585-2 – ident: e_1_2_8_47_1 doi: 10.1016/j.compstruct.2010.08.018 – ident: e_1_2_8_7_1 doi: 10.1016/j.enconman.2018.05.011 |
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This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a... This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building‐integrated... |
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| SubjectTerms | Air flow Air masses Algorithms BIPVT Datasets Decision trees Design parameters exergetic performance Exergy Feasibility studies Flow rates Learning algorithms Machine learning Mass flow rate Mathematical models Multilayer perceptrons Optimization Parameter identification Parameters Performance evaluation performance evaluation criteria Performance indices Photovoltaic cells Photovoltaics Prediction models predictive model Radial basis function Statistical analysis Support vector machines Thermodynamics Training |
| Title | Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors |
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