The effect of seasonal weather changes on the performance of databased models of the thermodynamic behaviour of buildings

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Several studies have indicated that Model Predictive Control (MPC) of space heating systems can utilize the thermal mass of residential buildings as short-term thermal storage for various demand response purposes. Realization of this potential relies heavily on the accuracy of the model used to represent the thermodynamics of the building. Such models, whether they are grey box or black box, are calibrated using relevant data obtained from initial measurements, and the performance of the calibrated model is validated using data from a subsequent period. However, many studies use validation periods with weather conditions similar to those of the calibration period. Only a few studies investigate whether the calibrated model performs satisfactory when subjected to significantly different conditions. This paper presents data from a simulation-based study on the effect of seasonal weather changes on the performance of a black-box model. The study was conducted using 11 years of Danish weather data (2008-2018). The results indicate that the performance of the black-box model deteriorate as the weather data conditions become increasingly different from those used in the initial model calibration. Further, the results show that calibration in heating season leads to satisfactory model performance through the heating season, but lower performance in transitional seasons (especially spring). Results also show that calibration in February led to highest model performance through heating season, while calibration in March led to satisfactory model performance in the whole heating and fall season.

BogserieE3S Web of Conferences
StatusUdgivet - jun. 2020
Begivenhed12th Nordic Symposium on Building Physics, NSB 2020 - Tallinn, Estland
Varighed: 6 sep. 20209 sep. 2020


Konference12th Nordic Symposium on Building Physics, NSB 2020

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