Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminan

Badalotti Passuello, Ana Carolina

Beninca, Letiane

Crespo Sánchez, Eva

English

UPCommons. Portal del coneixement obert de la UPC

MULTI-OBJECTIVE OPTIMIZATION OF RESIDENTIALMULTIFAMILY BUILDINGS TO MINIMIZE COOLING ANDHEATING SIMULTANEOUSLYMsc.  Let iane Benincá (UFRGS + UPC)Dra.  Eva Crespo Sánchez (UPC -  Catalonia)Dra .  Ana Carol ina Badalott i  Passuel lo (UFRGS -  Brazi l ) 04.05.2023 | RESEARCH CAFÉ ODS 7: LA NOVA ERA D’EDIFICIS EFICIENTS I SOSTENIBLESL’ENGINYERIA DE L’ENERGIA I L’ARQUITECTURA CREANT SINERGIES Introduct ionResearch problemMethodNSGA-I IMult i -object iveOpt imizat ionsConclusionsENSURE ACCESS TO AFFORDABLE, RELIABLE,SUSTAINABLE AND MODERN ENERGY FOR ALL THE SUSTAINABLE DEVELOPMENT GOALS REPORT 2022:  UNSTATS.UN.ORG/SDGS/REPORT/2022/Analysis of two scenarios (isolated and condominium) tocompare how the shadows of the surroundings can impact thedemand of the building.Improvement in the envelope utilized passive strategies.The energy demand for apartment positioning enhances thediscussion about energy vulnerability between neighbors.Implementation and evaluation of the presented multi-objectiveoptimization framework in case study buildings based on realmodels replicated worldwide.All open-access software were used for this experiment.Thesis novelty:INTRODUCTIONRESEARCH PROBLEMSantamouris and Vasi lakopoulou (2021), UN (2019;  2022),  and IPCC (2022)Economical impacts: Increase purchase levelsRising prices Low performance buildingsLack of manpowerECONOMYCHALLENGES TO BUILTENVIRONMENTBuilt environment impacts: Gas emissionsAmbiental impactsUrban heat islandsIncrease in energy consumptionBUILDINGSAnthropogenic impacts: Human activityGlobal warmingDesertificationNon-renewable resourcesGLOBALPopulation growth impacts: Urban pressureMore floor areaSocial inequaltiesEnergy povertySOCIETYMETHOD BUILDING GEOMETRYPRE-PROCESSINGOptimization definitions(code, EP objects, GA andBESOS)OPTIMIZATIONResults evaluationPOST-PROCESSINGMethod testing(optimization trials)RefineEvaluate methodRefineDefine settingsDevelop final method versionApplicationWEATHER FILERANGE OF VARIABLESNASGA-II SETTINGSCASE STUDIESRefineOptimal solutionsClimate mitigationAssess toolMETHODACTION SOFTWARE DEFINITIONSmodelbuilding:geometrythermal zonesenvelope configuration,building use, occupation,equipment's and lighting TERMOENERGETICSIMULATIONSConstruction Properties[NBR 15220/15575] Occupation scheduleLighting scheduleEquipment's loadNatural VentilationGroundMetabolic rate[INI-R] Weather file [BZ2]Simulation, optimization andoutcome resultsMinimize cooling demand (Dc)Minimize heating demand (Dh) ALL SOFTWARES ARE OPEN-ACESSNon-Dominated Sorting Genetic Algorithm II Proposed by Deb et al. (2002). Best suited for solving multi-objective optimization problemsof an architectural and engineering nature.Uses crowding distance, which means that it seeksconvergence of the fittest genes after the first randomgenerationManni and Nicolini, 2022; Hashempour et al., 2020; Costa-Carrapiço et al., 2020; Attia, 2013; Fortin, 2013 and Deb et al., 2002NSGA-II PARENT POPULATION (P)START CHILD POPULATION (Q) CHILD POPULATION (Q)  PARENT POPULATION (P) NEW POPULATIONENDPF3 PF1PF2PF3PARETO FRONTTERMINATIONCONDITIONCOMBINE PARENT ANDCHILD POPULATIONNON-DOMINATEDSORTINGCROWDINGDISTANCE SORTINGREJECTEDREJECTEDPF = PARETO FRONTNOManni and Nicolini, 2022; Hashempour et al., 2020; Costa-Carrapiço et al., 2020MULTI-OBJECTIVEMulti-objective analysis is a decision-making process that considersmultiple objectives or criteria to find the best possible solution. The Pareto Front is a graphical representation that shows the set ofoptimal solutions in a multi-objective analysis, where no othersolution can improve one objective without sacrificing another. In other words, it represents the trade-offs between differentobjectives and helps decision-makers identify the best solution fortheir needs.DC: Cooling demand (kWh/m².yr)DH: Heating demand (kWh/m².yr)OPTIMIZATIONSOPTIMIZATIONSCLIMATIC CONTEXT: CfaPHASE I: ISOLATED SCENARIOOptimal solar orientation97 kWh/m².yr 86 kWh/m².yrOptimal solar orientation with shadowsPHASE II: CONDOMINIUM SCENARIO86 kWh/m².yr 82 kWh/m².yrOptimal solar orientationOptimal solution: cooling and heating demands (kWh/m².yr)PHASE I AND II COMPARISONWindow-to-wall-ratio (WWR): Reference building, 15%, 20%and 25%.Optimization variables: Insulation thickness wall North, South,East and West. Insulation thickness roof. Solar absorptancewalls and roof. Glazing thickness. Setpoint ventilation.PHASE III: ENVELOPEWindow-to-wall-ratio (WWR): 20%.PHASE III: ENVELOPE31.7 kWh/m².yr 32.4 kWh/m².yrPHASE III: ENVELOPEPHASES COMPARISONCONCLUSIONSOptimizing multiple variables and balancing heating andcooling is crucial to achieving significant demand reduction ofup to 60% in buildings, as demonstrated by this study.The importance of analyzing multiple variables simultaneouslyand customizing solutions for buildings, rather thanstandardizing solutions, for optimal demand reduction.Utilizing energy-saving techniques, specifically enhancing thethermal envelope, is crucial to mitigate the effects of climatechange and address energy poverty in social housing, as thereare significant disparities in energy demand betweenapartments within the same building.Let iane Benincá[+34] 652 883 892benincalf@gmail.comhttps://orcid.org/0000-0003-2854-757Xhttps://futur.upc.edu/LetianeBenincaTHANK YOU FOR YOURATTENTION

Multi-objective optimization of residential multifamily buildings to minimize cooling and heating simultaneously

https://upcommons.upc.edu/bitstream/2117/387645/1/Letiane%20Beninca%20.pdf

Multi-objective optimization of residential multifamily buildings to minimize cooling and heating simultaneously

Abstract

Similar works

Full text

Available Versions

UPCommons. Portal del coneixement obert de la UPC