Model-based controllers for indoor climate control in office buildings – Complexity and performance evaluation

Gruber, Mattias; Trüschel, Anders; Dalenbäck, Jan-Olof

doi:10.1016/j.enbuild.2013.09.019

Cited by 41 publications

(11 citation statements)

References 17 publications

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“…In accordance to the common methodology that previously was applied in [18,19], the test equipment was equivalently placed along a straight line between the active pair of air diffusers (see Fig. 4).…”

Section: Methodsmentioning

confidence: 99%

“…The controller comparison was performed according to the method in [18] which is based on the two most important HVAC system functions: indoor climate is to be kept within given comfort ranges, by preferably using as little energy as possible [26]. The indoor climate aspect was taken into account by considering current standards and guidelines to formulate two comfort criterions (one each for IAQ and thermal climate) that both controllers were constrained to fulfill within feasible limits.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…This process was described in a previous publication [18] where the starting point was a controller with a complete energy balance of an office site and perfect information about all thermal disturbances in real-time. By predicting the influence of any changed condition, a perfect response in both time and magnitude for maintaining a desirable indoor climate could be achieved using as little energy as possible.…”

Section: Purpose and Proceduresmentioning

confidence: 99%

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Energy efficient climate control in office buildings without giving up implementability

2015

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h i g h l i g h t sThe principle of model-based controller for indoor climate control was adopted. A simplified version was presented and evaluated through experiments. A typical office working day was re-created in a test facility. Experiments were conducted for two weather seasons and two types of office rooms. Energy usage was reduced at the same time as the indoor climate was improved. Keywords:Office buildings Indoor climate control Implementability Air-based heating and cooling Energy efficiency Model-based controller a b s t r a c tThe adaptation between a building and its automation system can potentially be increased by model-based controllers with an integrated control model and information about indoor climate disturbances. The associated energy savings potential is large but a widespread utilization is typically prevented by high complexities. From that point of view, a trade-off technology that combines implementability with an overall higher performance than the system of current practice would be a better option at most sites. This work presents an experimental evaluation of an alternative controller that follows the same principle as model-based, but has gone through a large number of simplification measures for a reduced overall complexity and a limited function. The controller was evaluated for indoor climate control by automating the ventilation flow rate during a typical office working day that was re-created in a laboratory environment. Experiments were conducted in two different office sites, as well as during two weather seasons of Swedish summer and winter. From the investigation, it was concluded that despite of the reduced complexity, the investigated controller could save between 12% and 19% of indicated energy compared to a system of common practice at the same time as the quality of indoor climate was maintained.

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Methodsmentioning

confidence: 99%

Section: Purpose and Proceduresmentioning

confidence: 99%

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Energy efficient climate control in office buildings without giving up implementability

2015

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“…Progress in building technologies, weather forecasting and low-cost embedded computing systems pave the way for implementation of intelligent strategies with ''proactive'' and ''optimal'' capabilities for BOC applications. Advanced state-of-the-art implementations of BOC systems mostly rely on Model Predictive Control (MPC) [21][22][23], Co-simulation [24][25][26], popular optimizers [27,28] or neural networks [29][30][31][32][33][34]. Despite the recognized improvements over rule-based control strategies, such methods do not efficiently scale to large high-inertia BOC designs.…”

Section: Related Work and Contribution Of The Papermentioning

confidence: 99%

Proactive control for solar energy exploitation: A german high-inertia building case study

et al. 2015

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Abstract-Energy efficient passive designs and constructions have been extensively studied in the last decades as a way to 2 improve the ability of a building to store thermal energy, increase its thermal mass, increase passive insulation and 3 reduce heat losses. However, many studies show that passive thermal designs alone are not enough to fully exploit the 4 potential for energy efficiency in buildings: in fact, harmonizing the active elements for indoor thermal comfort with the 5 passive design of the building can lead to further improvements in both energy efficiency and comfort. These 6 improvements can be achieved via the design of appropriate Building Optimization and Control (BOC) systems, a task 7 which is more complex in high-inertia buildings than in conventional ones. This is because high thermal mass implies a 8 high memory, so that wrong control decisions will have negative repercussions over long time horizons. The design of 9 proactive control strategies with the capability of acting in advance of a future situation, rather than just reacting to 10 current conditions, is of crucial importance for a full exploitation of the capabilities of a high-inertia building. This paper 11 applies a simulation-assisted control methodology to a high-inertia building in Kassel, Germany. A simulation model of 12 the building is used to proactively optimize, using both current and future information about the external weather 13 I. INTRODUCTION 20Motivated by the fact that around half of the energy produced on the planet is used for the daily needs of building systems, 21 and floors are made to store, cage and distribute solar energy in the form of heat in the winter and reject solar heat in the 26 summer; passive cooling with different forms of ventilation and earth coupling; passive day-lighting to most effectively capture 27

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“…Its controller performance is highly sensitive to the accuracy of the model. Energy savings benefits of MPC for heating, ventilating, and air conditioning (HVAC) systems have been demonstrated in numerous papers [11][12][13][14][15][16][17]. However, up to now, the lack of dynamic simplified model of RCCUV system is preventing the application of MPC on RCCUV system.…”

Section: Introductionmentioning

confidence: 99%

A dynamic simplified model of radiant ceiling cooling integrated with underfloor ventilation system

Zhang

Xia

Cai

2016

Applied Thermal Engineering

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Model-based controllers for indoor climate control in office buildings – Complexity and performance evaluation

Cited by 41 publications

References 17 publications

Energy efficient climate control in office buildings without giving up implementability

Energy efficient climate control in office buildings without giving up implementability

Proactive control for solar energy exploitation: A german high-inertia building case study

A dynamic simplified model of radiant ceiling cooling integrated with underfloor ventilation system

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