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Influence of data pre-processing and sensor dynamics on grey-box models for space-heating: Analysis using field measurements

机译：Influence of data pre-processing and sensor dynamics on grey-box models for space-heating: Analysis using field measurements

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摘要

A grey-box model is a combination of data-driven and physics-based approaches to modeling. For applications in buildings, grey-box models can be used as the control model in model predictive control (MPC) or to characterize the thermal properties of buildings. In a previous study using data generated from virtual experiments, the influence of data pre-treatment on the performance of grey-box models has been demonstrated. However, field measurement differs from data generated using building performance simulation (BPS). This is because the precision and accuracy, the location, and the dynamics of the sensors could be different. Consequently, this paper extends previous results and conclusions using a real test case of a highly-insulated residential building. The results confirm that data pre-processing has a minimal influence on the identified results (parameter values and simulation performance) for deterministic models. On the contrary, data pre-treatment influences the performance of stochastic models as follows. Firstly, large sampling time (T-s) can cause the parameters to become non-physical and can sometimes reduce the one-day ahead prediction performance. With large T-s, the anti-causal shift (ACS) proves to be beneficial to keep the parameters physically plausible while low-pass filtering can also contribute but to a lesser extent. With large T-s, ACS does not guarantee a higher one-day ahead prediction performance for stochastic models, whereas pre-filtering generally has a positive impact. Secondly, for the stochastic model, the sensor dynamics should be modeled if the sensor has a noticeable time constant to guarantee the physical plausibility of the parameters. Thirdly, the dynamics of the hydronic radiator do not need to be modeled if the time constant in the temperature sensors is larger than the radiator. These findings provide practical guidelines for grey-box modeling of buildings with field measurement data.

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