Parameter Calibration for a TRNSYS BIPV Model Using In Situ Test Data

• Published in: Energies (Basel) Vol. 13; no. 18; p. 4935
• Main Authors: Ha, Sang-Woo, Park, Seung-Hoon, Eom, Jae-Yong, Oh, Min-Suk, Cho, Ga-Young, Kim, Eui-Jong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2020
• Subjects: Alternative energy sources; BIPV; Building envelopes; Calibration; Computer applications; Construction; Efficiency; Electricity; Energy; Energy consumption; Field tests; Green buildings; In situ measurement; Insulation; model parameter calibration; Model testing; Optimization; particle swarm optimization; Photovoltaics; Radiation; Renewable resources; Root-mean-square errors; Simulation; TRNSYS
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13184935

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, the requirement for appropriate operation and control of energy systems has become an important issue. To meet these requirements, a computational model is essential; however, some unmeasurable parameters can result in inaccurate results. This work proposes a calibration method for unknown parameters of a well-known BIPV model based on in situ test data measured over eight days; this parameter calibration was conducted via an optimization algorithm. The unknown parameters were set such that the results obtained from the BIPV simulation model are similar to the in situ measurement data. Results of the calibrated model indicated a root mean square error (RMSE) of 3.39 °C and 0.26 kW in the BIPV cell temperature and total power production, respectively, whereas the noncalibrated model, which used typical default values for unknown parameters, showed an RMSE of 6.92 °C and 0.44 kW for the same outputs. This calibration performance was quantified using measuring data from the first four days; the error increased slightly when data from the remaining four days were compared for the model tests.