Pregled bibliografske jedinice broj: 1275062
Development, Calibration, and Validation of a Simulation Model for Indoor Temperature Prediction and HVAC System Fault Detection
Development, Calibration, and Validation of a Simulation Model for Indoor Temperature Prediction and HVAC System Fault Detection // Buildings, 13 (2023), 6; 1388, 21 doi:10.3390/buildings13061388 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1275062 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Development, Calibration, and Validation of a Simulation Model for Indoor Temperature Prediction and HVAC System Fault Detection
Autori
Palaić, Darko ; Štajduhar, Ivan ; Ljubic, Sandi ; Wolf, Igor
Izvornik
Buildings (2075-5309) 13
(2023), 6;
1388, 21
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
building energy performance ; HVAC system ; smart-room system ; building modeling and simulation ; building thermal dynamics ; model validation and calibration ; genetic algorithm
Sažetak
An effective approach to increasing energy efficiency in buildings without compromising thermal comfort is to optimize heating, ventilation, and air conditioning (HVAC) systems through the use of advanced building-management system features, such as fault detection and diagnosis. Such functions are usually developed based on simulation models that must be calibrated and validated to achieve an appropriate level of accuracy and reliability. The objective of this study was to develop and calibrate a room-level simulation model of a hotel building and its HVAC system using TRNSYS 18 software and real data collected from the smart room system installed in the building. The calibration process was performed with 100 rooms using 5-min samples of room temperatures in selected 1-month periods during the summer and winter seasons by minimizing the root mean squared error (RMSE) in the average of the observed rooms using a genetic algorithm. The calibrated model was able to predict room temperatures with an RMSE of 0.79 ± 0.14 °C and a coefficient of variation in the root mean squared error (cvRMSE) of 3.58 ± 0.7%, which is well below the limits prescribed by international guidelines. The model was then applied to detect faults in the operation of fan coil units in the rooms based on the residual analysis and defined if–then rules. The results obtained show that the model can track the trends of temperature changes in real conditions and successfully detect major anomalies in a system.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Strojarstvo
POVEZANOST RADA
Projekti:
HAMAG-BICRO-HAMAG-BICRO KK.01.2.1.02.0303 - Adria Smart Room (ASR) (Štajduhar, Ivan, HAMAG-BICRO - Povećanje razvoja novih proizvoda i usluga koji proizlaze iz aktivnosti istraživanja i razvoja – faza II) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- Social Science Citation Index (SSCI)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus