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

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 modelled as a linear state-space system where the st...

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Bibliographic Details
Published inJournal of building performance simulation Vol. 14; no. 1; pp. 1 - 19
Main Authors Cupeiro Figueroa, Iago, Cimmino, Massimo, Drgoňa, Ján, Helsen, Lieve
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.01.2021
Taylor & Francis Ltd
Subjects
Computing time
fluid temperature prediction
Geothermal modelling
Kalman filter
Kalman filters
Load
load estimation
Load history
Model accuracy
moving horizon estimation
State observers
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Summary: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 modelled 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 five times more computational time.
ISSN:1940-1493
1940-1507
DOI:10.1080/19401493.2020.1838612