Improvement of borehole heat exchanger model performance by calibration using measured data

Puttige, Anjan Rao; Andersson, Staffan; Östin, Ronny; Olofsson, Thomas

doi:10.1080/19401493.2020.1761451

Cited by 10 publications

(10 citation statements)

References 31 publications

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“…In summer, the heat from space cooling and the excess heat from the heat pump are injected into the borehole heat exchanger; and in winter, the borehole heat exchanger acts as a source of heat for the heat pumps. A detailed explanation of the borehole heat exchanger and its model is presented in separate articles [31,32]. The geothermal heat pump was designed to satisfy 95% of the hospital's cooling load (5 GWh) and 20% of the heating load (7 GWh).…”

Section: Description Of the Studied Facilitymentioning

confidence: 99%

Application of Regression and ANN Models for Heat Pumps with Field Measurements

et al. 2021

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Developing accurate models is necessary to optimize the operation of heating systems. A large number of field measurements from monitored heat pumps have made it possible to evaluate different heat pump models and improve their accuracy. This study used measured data from a heating system consisting of three heat pumps to compare five regression and two artificial neural network (ANN) models. The models’ performance was compared to determine which model was suitable during the design and operation stage by calibrating them using data provided by the manufacturer and the measured data. A method to refine the ANN model was also presented. The results indicate that simple regression models are more suitable when only manufacturers’ data are available, while ANN models are more suited to utilize a large amount of measured data. The method to refine the ANN model is effective at increasing the accuracy of the model. The refined models have a relative root mean square error (RMSE) of less than 5%.

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Section: Description Of the Studied Facilitymentioning

confidence: 99%

Application of Regression and ANN Models for Heat Pumps with Field Measurements

et al. 2021

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“…Table 1 contains a summary of the properties of the BHE. In Puttige et al [54], a more detailed description of the properties of the BHE.…”

Section: Gshp Installationmentioning

confidence: 99%

“…Therefore, it is better suited than the hybrid model presented in this study to identify measurement errors. The analytical model developed by Puttige et al [54] with one-hour time resolution was used. Section 3 includes a short explanation of the model.…”

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confidence: 99%

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A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

et al. 2020

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Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

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“…Moreover, the measurement instruments (e.g., calorimeters) normally measure fluid heat flow rateQ f , which does not necessarily coincide with the ground heat flow rateQ b as depicted in Figure 1. Finally, there is always a degree of uncertainty related not only to the measurement data but also to model parameters that can be re-estimated after a period of operation [24] or to the presence of neighbouring boreholes that can interfere with the ground load history [25].…”

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Fluid temperature predictions of geothermal borefields using load estimations via state observers

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et al. 2020

Journal of Building Performance Simulation

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Fluid temperature predictions of geothermal borefields usually involve temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modeled as a linear state space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history to obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 • C and 1 • C respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile estimates at the expense of about 5 times more computational time.

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Improvement of borehole heat exchanger model performance by calibration using measured data

Cited by 10 publications

References 31 publications

Application of Regression and ANN Models for Heat Pumps with Field Measurements

Application of Regression and ANN Models for Heat Pumps with Field Measurements

A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

Fluid temperature predictions of geothermal borefields using load estimations via state observers

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