Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm

Schmelas, Martin; Feldmann, Thomas; Bollin, Elmar

doi:10.1016/j.enbuild.2015.06.012

Cited by 41 publications

(16 citation statements)

References 13 publications

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“…The magnitude of both parameters can be interchanged to obtain the required power [90]. The most common controlling parameters are outdoor temperature with or without indoor temperature feedback and indoor dew-point [48,51,85] and the relation between controlled variables and controlling parameters is defined by the control strategies. Advanced control strategies might use weather forecast or historical data to define the controlled variables.…”

Section: Tabs Control Strategiesmentioning

confidence: 99%

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Simulation and control of thermally activated building systems (TABS)

Romaní

Gracia

Cabeza

2016

Energy and Buildings

138

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Buildings account for a significant amount of global energy use and CO 2 emissions. Thermally Activated Building Systems (TABS) are a technology with potential for significantly reducing buildings energy use. TABS are heating and cooling systems that are integrated in the building structure. They mainly exchange heat through radiation and are able to store heat in the building thermal mass. TABS high thermal mass and their interaction with the building structure make their energy evaluation and design process difficult. Development of simulation models has been essential to study the design and control of TABS. Control of TABS is challenging due to the slow response time and storage capacity. A lot of research has been conducted to develop control strategies that fully exploit its energy saving potential and that maximise the use of renewable energies. This paper summarizes the main characteristics of TABS and presents the developed simulation models and control strategies.

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Section: Tabs Control Strategiesmentioning

confidence: 99%

“…A more developed control was presented by Schmelas et al [85] in the form of the AMLR, an adaptive and predictive algorithm for control of TABS. The control is based in multiple linear regressions and uses Ordinary Least Squares method (OLS) for calculating the regression coefficients.…”

Section: Adaptive and Predictive Controlsmentioning

confidence: 99%

Simulation and control of thermally activated building systems (TABS)

Romaní

Gracia

Cabeza

2016

Energy and Buildings

138

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“…Thus, the TABS can handle only a limited amount of heating and cooling loads in office buildings using self-regulation. Many previous studies attempted to remove the loads with the TABS as much as the expected load handled with the radiant system [13][14][15][16][17][18][19][20]. However, this paper focuses on the proper functioning of TABS.…”

Section: Application Of Self-regulation Using the Tabsmentioning

confidence: 99%

Control of Thermally Activated Building System Considering Zone Load Characteristics

Chung

Park²,

Yeo

et al. 2017

Sustainability

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Abstract:The objectives of this study were to investigate the thermally activated building system (TABS) mechanism for appropriate use of the system and to apply the proper concept of TABS for each zone by using different TABS control strategies. In order to examine the TABS mechanism, dynamic simulation with EnergyPlus was used to model the office building with TABS, because the radiant heat exchange characteristics of the TABS according to the time variable was critical. The typical control concept of TABS, self-regulation, was applied in the simulation by setting the supply water temperature as room setpoint temperature. As a result, the advantage of self-regulation can be amplified by utilizing the entire thermal mass of the TABS, which can be executed by customizing to target a specific type of load. Since the large area of the office building may comprise different loads in different zones, the TABS control according to the different zone loads were proposed. By separating the control strategy from zone to zone, the proposed control strategy improved the thermal comfort by 5%, reduced peak heating load by 10%, reduced cooling load by 36%, and decrease the total energy consumption by 13%. This study demonstrated a possible improvement on self-regulation of TABS with separate zone controls.

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“…were studied coupled to gain scheduling control (GSC) [19], pulse width modulation (PWM) [20], adaptive predictive control [21], and MPC [13,22], among others, all showing improved performance compared to common base case controls. Finally, MPC was highlighted as a control scheme with good potential for optimizing TABS operation, although further research is needed [8].…”

Section: Introductionmentioning

confidence: 99%