A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings

Joe, Jaewan; Karava, Panagiota

doi:10.1016/j.apenergy.2019.03.209

Cited by 164 publications

(41 citation statements)

References 39 publications

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“…Significant energy/cost-saving potential has been demonstrated in simulations and actual implementation in previous studies. The electricity cost was reduced by more than 30% in the cooling season compared to typical feedback control with a radiant floor system through implementation in an actual office building [16]. Cooling energy was reduced by approximately 13% through optimized RTU coordination in a small retail store [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Joe

Dong

2020

Sustainability

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This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

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Section: Literature Reviewmentioning

confidence: 99%

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Joe

Dong

2020

Sustainability

Self Cite

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“…In particular, the first logic is implemented with the aim to improve the real-time management of storage tanks [17], boilers [18], district heating systems [19][20]. For instance, in [21], with reference to the heating and cooling systems of office buildings, the model predictive control is applied to improve the indoor microclimate control. On the other hand, with reference to the rule-based and association rules, there are a number of control strategies, like the occupancy-based heating control proposed by Shin et al [22] for the optimal start and stop control of radiant heating systems to improve the energy saving and the thermal comfort.…”

Section: Introduction and Literature State Of The Artmentioning

confidence: 99%

Evaluation and optimization of the performance of the heating system in a nZEB educational building by monitoring and simulation

Borrelli

Merema

Ascione

et al. 2021

Energy and Buildings

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The optimal control of the HVAC system in the nearly zero energy buildings (nZEBs) can be considered as a challenge.Indeed, the lack of data given by monitored data does not allow understanding what are the implications of different strategies on thermal comfort and energy consumptions. This paper determines an approach for the evaluation of the management of the heating system in an existing educational nZEB. The aim is to understand how the energy efficiency of the heating system is sensitive to controls strategies and the important role played by the control systems. The test lecture rooms on KU Leuven Ghent Technology Campus (Belgium) have been chosen as case study.A deep analysis, starting from an in-field monitoring, was carried out during the winter period of 2019, in order to assess the actual operation and the energy use of building systems under real conditions. Then, a building energy simulation (BES) model of the building and its systems was developed and calibrated against the real monitoring data. Control strategies of the heating system are simulated and compared to the current configuration. The simulation results show that strategies based on a definition of a heating curve for the boiler can reduce the comfortable time (-23%). Controlling the temperature inside the storage tank as a function of the outdoor temperature together with the control on the activation of the air handling unit result in an energy savings from 32% to 46%, with a correspondent increase of comfort time in the period from 0.6% to 3.4% compared to the baseline scenario, depending to the relationship between the external temperature and water temperature inside the water storage tank.

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“…[7] has suggested a vector field-based support vector regression scheme to predict the building energy * Corresponding author: yuemin.ding@ntnu.no use of a large office building. Meanwhile, regarding energy optimization, [8] has introduced a model predictive control strategy to optimize the energy use of floor heating and cooling systems based on data collected from an actual office building. [9] has proposed a cooperative energy management scheme between buildings and districts with data-driven and multistage stochastic optimization.…”

Section: Introductionmentioning

confidence: 99%

Data-driven analysis of electricity use for office buildings: a Norwegian case study

Ding

Nord

2021

E3S Web Conf.

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Buildings are major consumers of primary energy and main contributors to carbon emission. To improve energy efficiency, it is essential to understand the characteristics of energy use in buildings. This study uses an in-use office building with digital systems for monitoring and control in Trondheim, Norway, as the study case. Based on data collected from this office building, a data-driven analysis was conducted to capture the characteristics of electricity use of different parts in the office building. The approaches used in this study included statistical analysis and polynomial regression. The impact of occupancy level on the total electricity use, the electricity use in office areas, and that in corridors & meeting rooms was also studied. The hourly electricity use profiles were obtained for ventilation fans and the cantina. In the end, the electricity use characteristics and existing issues in this office building were discussed.

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A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings

Cited by 164 publications

References 39 publications

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Evaluation and optimization of the performance of the heating system in a nZEB educational building by monitoring and simulation

Data-driven analysis of electricity use for office buildings: a Norwegian case study

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