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Energies2018,11, 1900
This smallweather station is located inTianjinUniversity, China,which consists of aPC-2-T
solar radiationobserver, aPC-4meteorologicalmonitoringrecorder, transducersandthemanagement
software of theweather stationmonitoring system. It records theweather data every half hour
by thesedevices and transfersdata to the computer viawired cables. Theweatherdata collected
bythemeteorological stationmainly includedry-bulb temperature, relativehumidity,windspeed,
winddirection, sunshinehours, rainfall, solar radiation intensity. In addition, among theweather
parameters frommostexistingweatherwebsites, thepredictionaccuraciesof thedry-bulbtemperature
and the relative humidity are relatively high,while the prediction accuracies of parameters such
aswind speed, winddirection and solar radiation intensity are poor. Some cannot be predicted
in advance, suchas solar radiation intensity. Most of theprevious literature selected temperature
andrelativehumidityas inputs to establish thepredictionmodel [14–16]. Therefore, in thispaper,
wemainlyrecordedthehourlyweather forecastdata fromtheweatherwebsite, includingdry-bulb
temperatureandrelativehumidity,anddiscussedthe influenceof theuncertaintyof forecastdry-bulb
temperatureandrelativehumidityonthecooling loadforecast.
3.3. TheDBModel of theOfficeBuilding
Thecaseselected in thisarticle isanofficebuilding inTianjinCity, located inBinhaiNewDistrict,
Tianjin,withaconstructionareaof10,723.16squaremeters,buildingheightof22.80m,5floorsabove
ground,1floorundergroundandaroofsetwithskylights.
ThefinalmodelcreatedbytheDesignBuildersoftwareversion4.2.0.015isshowninFigure4.DBis
themostcomprehensiveGraphicalUserInterfacetotheEnergyPlussimulationenginewhichiswidely
usedformodeling[35]. Parametersof thebuildingstructureareobtainedthroughresearch,andother
parameters refer to“TianjinPublicBuildingEnergyEfficiencyDesignStandards” (DB29-153-2014) for
setting, suchaspersonneldensity,personnelperroomrate, lightingdensity, runningtime. Theheat
sourceissuppliedbythedistrictheatingpipenetworkinwinter,andtheterminaloftheairconditioning
systemis thefancoil system,while ituses thesplitVariableRefrigerantVolume(VRV)airconditioning
system for cooling in summer. It is difficult to obtain the hourly cooling load bymeasurement.
Inaddition, theHVACsystemsof theofficebuildingarenormallyusedfromMondaytoFridayand
arenotusedonweekendsandholidays. Therefore,only the loads from9a.m. to5p.m. onweekdays
areconsidered in thescopeof thestudyof loadforecasting. Theerroranalysisof thesimulatedhourly
heatingloadandthemeasuredheatingsupplydata from9:00to17:00for threeworkingdays iscarried
out toverify thesimulation.
Figure4.TheofficebuildingmodelbuiltbyDesignBuilder.
Theresult is showninFigure5. Theaveragerelativeerrorbetweenthemeasureddataandthe
simulateddatawas16.1%,which isacceptableconsideringof the limitationsof theon-site testsand
measurement instruments. Therefore, thesimulation loadcanberegardedas thereal loadtoestablish
thedatabase.
219
Short-Term Load Forecasting by Artificial Intelligent Technologies
- Titel
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Autoren
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 448
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorie
- Informatik