Abstract
This paper presents a new parameter identification (PI) algorithm for estimating effective and detailed thermal parameters of ground source heat pump systems using data obtained from the well-known thermal response test. The PI comprises an iterative scheme coupling a semi-analytical forward model to an inverse model. The forward model is formulated based on the spectral element method to simulate transient 3D heat flow in ground source heat pump (GSHP) systems, and the inverse model is formulated based on the interior-point optimization method to minimize the system objective function. Compared to existing interpretation tools for the thermal response test, the proposed PI algorithm has several advanced features, including: it can handle fluctuating heat pump power and inlet temperatures; interpret data obtained from multiple heat injection or extraction signals; produce accurate backcalculation for short and long duration experiments; and handle multilayer systems. The PI algorithm is tested against synthesized data, using a wide range of random noise, and versus an available laboratory experiment. The computational results show that the PI algorithm is accurate, stable and exhibiting relatively high convergence rate.
Highlights
The use of the ground source heat pump technology for heating and cooling of buildings is rapidly rising worldwide, and engineers are striving to improve its design
This paper presents a new parameter identification (PI) algorithm for estimating effective and detailed thermal parameters of ground source heat pump systems using data obtained from the well-known thermal response test
The forward model is formulated based on the spectral element method to simulate transient 3D heat flow in ground source heat pump (GSHP) systems, and the inverse model is formulated based on the interior-point optimization method to minimize the system objective function
Summary
The use of the ground source heat pump technology for heating and cooling of buildings is rapidly rising worldwide, and engineers are striving to improve its design. An important element of the thermal response test is the interpretation procedure of the measured data This experiment is relatively expensive but can only be useful if the utilized parameter identification algorithm is able to produce accurate estimate of the involved thermal parameters. The consistency, stability, convergence rate and uniqueness of the inverse model for solving the objective functions Based on these factors, parameter identification algorithms utilized for TRT interpretation can be put in three categories: 1. This method is widely used because of its simplicity; simple mathematical tools (such as MS Excel) can be used to interpret the measured data. We elaborate on the challenges involved in interpreting the TRT measurements (Section 2) and discuss the current GI algorithm (Section 3)
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