Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction

Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize...
Ausführliche Beschreibung

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Autor*in:	Boithias, Florent [verfasserIn] El Mankibi, Mohamed Michel, Pierre

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	neural networks optimization genetic algorithms discomfort prediction energy consumption prediction

Anmerkung:	© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011

Übergeordnetes Werk:	Enthalten in: Building simulation - Beijing : Tsinghua Press, 2008, 5(2012), 2 vom: 16. Jan., Seite 95-106
Übergeordnetes Werk:	volume:5 ; year:2012 ; number:2 ; day:16 ; month:01 ; pages:95-106

Links:	Volltext

DOI / URN:	10.1007/s12273-012-0059-6

Katalog-ID:	SPR024698334

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520			\|a Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions.
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650		4	\|a discomfort prediction \|7 (dpeaa)DE-He213
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700	1		\|a El Mankibi, Mohamed \|4 aut
700	1		\|a Michel, Pierre \|4 aut
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912			\|a GBV_ILN_187
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912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
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912			\|a GBV_ILN_2027
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
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allfields	10.1007/s12273-012-0059-6 doi (DE-627)SPR024698334 (SPR)s12273-012-0059-6-e DE-627 ger DE-627 rakwb eng Boithias, Florent verfasserin aut Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213 El Mankibi, Mohamed aut Michel, Pierre aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 5(2012), 2 vom: 16. Jan., Seite 95-106 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:5 year:2012 number:2 day:16 month:01 pages:95-106 https://dx.doi.org/10.1007/s12273-012-0059-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2012 2 16 01 95-106
spelling	10.1007/s12273-012-0059-6 doi (DE-627)SPR024698334 (SPR)s12273-012-0059-6-e DE-627 ger DE-627 rakwb eng Boithias, Florent verfasserin aut Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213 El Mankibi, Mohamed aut Michel, Pierre aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 5(2012), 2 vom: 16. Jan., Seite 95-106 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:5 year:2012 number:2 day:16 month:01 pages:95-106 https://dx.doi.org/10.1007/s12273-012-0059-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2012 2 16 01 95-106
allfields_unstemmed	10.1007/s12273-012-0059-6 doi (DE-627)SPR024698334 (SPR)s12273-012-0059-6-e DE-627 ger DE-627 rakwb eng Boithias, Florent verfasserin aut Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213 El Mankibi, Mohamed aut Michel, Pierre aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 5(2012), 2 vom: 16. Jan., Seite 95-106 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:5 year:2012 number:2 day:16 month:01 pages:95-106 https://dx.doi.org/10.1007/s12273-012-0059-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2012 2 16 01 95-106
allfieldsGer	10.1007/s12273-012-0059-6 doi (DE-627)SPR024698334 (SPR)s12273-012-0059-6-e DE-627 ger DE-627 rakwb eng Boithias, Florent verfasserin aut Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213 El Mankibi, Mohamed aut Michel, Pierre aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 5(2012), 2 vom: 16. Jan., Seite 95-106 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:5 year:2012 number:2 day:16 month:01 pages:95-106 https://dx.doi.org/10.1007/s12273-012-0059-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2012 2 16 01 95-106
allfieldsSound	10.1007/s12273-012-0059-6 doi (DE-627)SPR024698334 (SPR)s12273-012-0059-6-e DE-627 ger DE-627 rakwb eng Boithias, Florent verfasserin aut Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213 El Mankibi, Mohamed aut Michel, Pierre aut Enthalten in Building simulation Beijing : Tsinghua Press, 2008 5(2012), 2 vom: 16. Jan., Seite 95-106 (DE-627)564750867 (DE-600)2422327-X 1996-8744 nnns volume:5 year:2012 number:2 day:16 month:01 pages:95-106 https://dx.doi.org/10.1007/s12273-012-0059-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2012 2 16 01 95-106
language	English
source	Enthalten in Building simulation 5(2012), 2 vom: 16. Jan., Seite 95-106 volume:5 year:2012 number:2 day:16 month:01 pages:95-106
sourceStr	Enthalten in Building simulation 5(2012), 2 vom: 16. Jan., Seite 95-106 volume:5 year:2012 number:2 day:16 month:01 pages:95-106
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	neural networks optimization genetic algorithms discomfort prediction energy consumption prediction
isfreeaccess_bool	false
container_title	Building simulation
authorswithroles_txt_mv	Boithias, Florent @@aut@@ El Mankibi, Mohamed @@aut@@ Michel, Pierre @@aut@@
publishDateDaySort_date	2012-01-16T00:00:00Z
hierarchy_top_id	564750867
id	SPR024698334
language_de	englisch
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author	Boithias, Florent
spellingShingle	Boithias, Florent misc neural networks misc optimization misc genetic algorithms misc discomfort prediction misc energy consumption prediction Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
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topic_title	Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction neural networks (dpeaa)DE-He213 optimization (dpeaa)DE-He213 genetic algorithms (dpeaa)DE-He213 discomfort prediction (dpeaa)DE-He213 energy consumption prediction (dpeaa)DE-He213
topic	misc neural networks misc optimization misc genetic algorithms misc discomfort prediction misc energy consumption prediction
topic_unstemmed	misc neural networks misc optimization misc genetic algorithms misc discomfort prediction misc energy consumption prediction
topic_browse	misc neural networks misc optimization misc genetic algorithms misc discomfort prediction misc energy consumption prediction
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title	Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
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title_full	Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
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author_browse	Boithias, Florent El Mankibi, Mohamed Michel, Pierre
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doi_str_mv	10.1007/s12273-012-0059-6
title_sort	genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
title_auth	Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
abstract	Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011
abstractGer	Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011
abstract_unstemmed	Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. Conclusion is that prediction at a 2-hour “distance” and a 3-day quantity of data are the best tested optimization conditions. © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011
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title_short	Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction
url	https://dx.doi.org/10.1007/s12273-012-0059-6
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up_date	2024-07-04T02:01:05.108Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR024698334</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230403075413.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12273-012-0059-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR024698334</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12273-012-0059-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Boithias, Florent</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Growing concerns about energy consumption reduction and comfort improvement inside buildings make it more and more necessary to be able to predict with fine precision building’s heating loads and indoor discomfort. This article proposes a method based on genetic algorithms (GAs) to optimize the architecture, training parameters and inputs of an artificial neural network (ANN). The ANN is doomed to predict energy consumption and indoor discomfort in future work on the development of an on-line method for control setting optimization. Simple and advanced controllers were used in this study: ON-OFF, PID and fuzzy controllers. Validation of the optimized ANN showed good prediction accuracy, as regression coefficients R2 for consumption and discomfort were respectively greater than 0.77 and 0.84 for the three tested controllers. Various prediction “distances” and ANN training data quantities were tested. 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Genetic algorithms based optimization of artificial neural network architecture for buildings’ indoor discomfort and energy consumption prediction

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