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Campus Feuchtwangen , Hochschule Ansbach , christophtschi@hs-ansbach.de

Campus Feuchtwangen , Hochschule Ansbach , christoph.catschi@hs-ansbach.de. Christoph MATSCHI , Gernot VOGT, Isabell NEMETH. Identification Of Characteristic Thermal Load Profiles Of Different Use Areas In Non-residential Buildings. Introduction. CleanTechCampus (CTC)

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Campus Feuchtwangen , Hochschule Ansbach , christophtschi@hs-ansbach.de

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  1. Campus Feuchtwangen, HochschuleAnsbach, christoph.catschi@hs-ansbach.de Christoph MATSCHI, Gernot VOGT, Isabell NEMETH Identification Of Characteristic Thermal Load Profiles Of Different Use Areas In Non-residential Buildings

  2. Introduction • CleanTechCampus (CTC) • Energy concept for the campus Garching of the TU-Munich • Optimization of the plant, district heating grid, electric grid… Figure 1: Campus Garching todayandprojection in 2050 • HS-Ansbach: prediction of the heat load demand of the campus Garching 1331

  3. Introduction • In EU approximately 36 % of greenhouse gas emission and 40 % of total energy demand are attributable to the building sector for cooling, heating and ventilating • greatpotential forsavingenergyandgreenhousegases in thebuildingsector possibleactivitiestoutilizethis potential: • an energeticallyoptimizedbuildingenvelope • an optimizationoftheenergyproduction • anoptimizationoflocalanddistrictheatinggridsbybetterknowledgeofenergydemandwithinthedistricts • an optimizationoflocalanddistrictheatinggridsbybetterknowledgeofenergydemandwithinthedistricts 1331

  4. Introduction • Knowledge aboutthetemporally high-resolution courseoftheloadaswellasthesimultaneityoftheheatdemand (coincidencefactor) isofcentralimportance • Coincidencefactorcommonlydeterminedbystaticmethods in combinationwithsafetyfactors • thequalityofpredictedheatloadprofiles, stronglydepends on theexperiencesoftheplanner • systemsdo not operate in thepointof optimal energeticefficiency • implementrenewableenergies in districtheatingnetworks in a proper wayisdifficult • knowledgeaboutthetemporally high-resolution courseoftheloadaswellasthesimultaneityoftheheatdemandisofcentralimportance 1331

  5. Introduction • Simulation of a district • time consuming • expensive • A fast and easy methodetocreateheatloadprofilesisneeded • top-down modells (based on historicaldata) • bottom-upmodells • knowledgeaboutthetemporally high-resolution courseoftheloadaswellasthesimultaneityoftheheatdemandisofcentralimportance Tab. 2: Examplesofbottom-upmodells 1331

  6. Introduction • Aimofthisresearchworkistocreateheatdemandprofileswithappropriateaccuracylikewisea thermal simulationwhereall usageunitsareassessedindividually, but withtheadvantageof a less time-consumingsetup 1331

  7. Method & Results • Simulation ofthefourlargestbuildingsofthequarter • This buildingsapproximatelyresponsiblefor75% ofthe total energyconsumtion Figure2: Campus Garching today 1331

  8. Method & Results • Zoned in different usageunitsbased on DIN V 18599 • Thermal simulationwiththetool AX3000 (based on EnergyPlus) • All different zonessimulated => developed a meanheatloadprofileforeachusageunit Figure3: Zoneschemistrybuilding Campus Garching 1331

  9. Method & Results Meanheatloadprofilforeachzone/usageunitaccordingtobuildingageclass (U-value) and thermal mass • meanwindowarea • meancompactness • meandirection 1331

  10. Method & Results Test iftherearediffenecesbetweenthe individual usageunits Figure 4: Mean of differences and standard deviation of differences of usage units to office unit within BA1 over one year Figure 5: Heat load differences of the individual units to office unit over one year within BA1 1331

  11. Method & Results (laboratoryareaofthebuilding/district) [m²] (officeareaofthebuilding/district) [m²] 1331

  12. Method & Results (libraryareaofthebuilding/district) [m²] … Super positionedloadprofilefortheCampus 1331

  13. Validation Simulated Measured Figure 6: Compared annual load duration curves 1331

  14. Validation Simulated Measured Figure 7: Super positioned heat load profile andmeasuredheadloadprofileoftheheatgenerator at the Campus 1331

  15. Conclusion • Itispossibletodefinedifferent characteristicalusageunits • Withina greaterdistrict a feasiblescalingandaggregationoftheaccompaniedheatloadprofilescanbeconducted • Itisshown, thatthismethodleadsto a comprehensiveestimationofthe total heatloadprofileofthewholedistrict • The method seems to work What´s left to do • Influencingfactors(storagemass, windowarea, buildinggeometryanduserbehavior) must beanalyzedseparatelyandimplemented in theheatloadprofiles • Transient simulationshavetobeaccomplishedforusageunits like classrooms, shop, residentialuse, etc. toexpandtherangeofapplicationofthismethodtoanyotherdistrict. 1331

  16. MATSCHI Christoph Christoph.Matschi@hs-ansbach.de 1331

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