This section provides downloads and links to articles, papers, reports and diagrams, plus relevant and related guides.
The project deliverables will also be accessible here, and shall be added to whilst the project progresses.
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On 18th November our partner, Dr. Eline Himpe (Ghent University), introduced and presented our project to the UK division of the BEIS/IEA Heat Pump Meeting.
It was positively-received and clear in explaining project, concept and future plans for hybridGEOTABS and our planned manual, webtool and Knowledge Centre.
The slides are available to download and read below.
Download the slides here
Activating the thermal mass of a building by implementing Thermally Active Building Systems (TABS) assists in reducing energy use for thermal management of buildings by utilizing a low temperature heating and high temperature cooling approach. Coupling TABS with geothermal heat pumps that use low-grade energy source in addition to model-based predictive control (MPC) helps to further decrease energy use. Most equipment in hybrid GEOTABS buildings follow a modular structure that can be classified as low, medium and high temperature sources, and emission systems depending on the building type and needs. This work describes the main characteristics of the individual modules and interfaces of hybrid GEOTABS buildings, and provides examples of three types of buildings that use the hybrid GEOTABS approach. These buildings are an elementary school in the Czech Republic, an elderly care home in Belgium, and an office building in Luxembourg. Although these buildings are functionally different, the generic hybrid GEOTABS concept can be abstracted based on a detailed consideration of the interaction between energy transfer systems (e.g. geothermal heat exchangers, heat pumps, boilers) and emission systems (e.g. TABS, air handling units, radiators, domestic hot water). This work defines the generic concept, individual modules, and interfaces between related components of hybrid GEOTABS, enabling the specification of a design template with a “minimum” number of required operational parameters. Such a template can enable fast sizing of major system components, consistency between design-build offers, and facilitate effective integration of the Hybrid GEOTABS into new buildings.
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Model Predictive Control (MPC) predictive’s nature makes it attractive for controlling high-capacity structures such as thermally activated building systems (TABS). Using weather predictions in the order of days, the system is able to react in advance to changes in the building heating and cooling needs. However, this prediction horizon window may be sub-optimal when hybrid geothermal systems are used, since the ground dynamics are in the order of months and even years. This paper proposes a methodology that includes a shadow-cost in the objective function to take into account the long-term effects that appear in the borefield. The shadow-cost is computed for a given long-term horizon that is discretized over time using predictions of the building heating and cooling needs. The methodology is applied to a case with only heating and active regeneration of the ground thermal balance. Results show that the formulation with the shadow cost is able to optimally use the active regeneration, reducing the overall operational costs at the expenses of an increased computational time. The effects of the shadow cost long-term horizon and the predictions accuracy are also investigated.Download the paper here
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 0.1C and 1C, 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.Link to paper abstract