LEADER 03874nam  22006855  450 
001 9910299595803321
005 20230504012836.0
010   $a3-319-90146-X
024 7 $a10.1007/978-3-319-90146-6
035   $a(CKB)4100000003359665
035   $a(MiAaPQ)EBC5355989
035   $a(DE-He213)978-3-319-90146-6
035   $a(PPN)226697312
035   $a(EXLCZ)994100000003359665
100   $a20180420d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEnergy Optimization and Prediction in Office Buildings $eA Case Study of Office Building Design in Chile /$fby Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (89 pages)
225 1 $aSpringerBriefs in Energy,$x2191-5539
311   $a3-319-90145-1 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Research Method -- Energy Demand Analysis -- Multiple Linear Regressions -- Artificial Neural Networks -- Conclusions.
330   $aThis book explains how energy demand and energy consumption in new buildings can be predicted and how these aspects and the resulting CO2 emissions can be reduced. It is based upon the authors? extensive research into the design and energy optimization of office buildings in Chile. The authors first introduce a calculation procedure that can be used for the optimization of energy parameters in office buildings, and to predict how a changing climate may affect energy demand. The prediction of energy demand, consumption and CO2 emissions is demonstrated by solving simple equations using the example of Chilean buildings, and the findings are subsequently applied to buildings around the globe. An optimization process based on Artificial Neural Networks is discussed in detail, which predicts heating and cooling energy demands, energy consumption and CO2 emissions. Taken together, these processes will show readers how to reduce energy demand, consumption and CO2 emissions associated with office buildings in the future. Readers will gain an advanced understanding of energy use in buildings and how it can be reduced.
410  0$aSpringerBriefs in Energy,$x2191-5539
606   $aSustainable architecture
606   $aEnergy policy
606   $aEnergy and state
606   $aBuildings?Design and construction
606   $aNeural networks (Computer science)
606   $aMathematical optimization
606   $aSustainable Architecture/Green Buildings
606   $aEnergy Policy, Economics and Management
606   $aBuilding Construction and Design
606   $aMathematical Models of Cognitive Processes and Neural Networks
606   $aOptimization
615  0$aSustainable architecture.
615  0$aEnergy policy.
615  0$aEnergy and state.
615  0$aBuildings?Design and construction.
615  0$aNeural networks (Computer science).
615  0$aMathematical optimization.
615 14$aSustainable Architecture/Green Buildings.
615 24$aEnergy Policy, Economics and Management.
615 24$aBuilding Construction and Design.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
615 24$aOptimization.
676   $a725.23
700   $aRubio-Bellido$b Carlos$4aut$4http://id.loc.gov/vocabulary/relators/aut$0998278
702   $aPérez-Fargallo$b Alexis$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aPulido-Arcas$b Jesús$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299595803321
996   $aEnergy Optimization and Prediction in Office Buildings$92289776
997   $aUNINA