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Predicting Thermal Load Profiles for Energy Optimization in Non-residential Buildings

Explore characteristic thermal load profiles in non-residential buildings to optimize energy usage. Research focuses on predicting heat demand for efficient district heating grids. Develop fast methods for creating heat load profiles. Simulation results analyzed for building zones. Potential for energy savings and reducing greenhouse emissions. Conclusion suggests possible scaling for broader district applications.

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Predicting Thermal Load Profiles for Energy Optimization in Non-residential Buildings

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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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