Learning/repetitive control for building systems with nearly periodic disturbances

Vinther, Kasper; Chandan, Vikas; Alleyne, Andrew G.

doi:10.23919/ecc.2013.6669338

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…The fundamentals of RC is that a certain system which performs a certain activity several times can increase its performance from preceding repetitions (executions). Only a small number of papers consider this periodic behavior so as to prevent its impact on thermal comfort [31,32]; in other works, the RC is used to counteract periodic effects of the weather [33,34]. The control approach proposed in this paper has been tested by means of simulation tests, employing a room simulator which makes use of a non-linear model based on first principles [35].…”

Section: Novelty Of This Workmentioning

confidence: 99%

Repetitive Control to Improve Users’ Thermal Comfort and Energy Efficiency in Buildings

2018

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The development of control systems to reach and maintain optimal thermal comfort inside buildings is a research topic which has received great attention in the last several years because people's productivity is linked to their comfort. The main weakness of these control systems is that they do not take into account that people affect their own thermal comfort, since humans are heat sources that increase the temperature inside buildings. For this reason, people can be considered as disturbances to thermal comfort control systems. Usually, people follow a timetable along the week, so they enter and leave buildings at the same time every day. Taking this behavior into account can be very useful in improving people's thermal comfort. In this paper, a repetitive control (RC) approach that makes use of this information to anticipate the effects of people on indoor temperature is presented. Besides that, the control system includes a proportional-integral (PI) controller in charge of counteracting the non-periodic disturbances. Simulation results obtained with this control system through a room simulator are presented in order to show its efficiency.

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Section: Novelty Of This Workmentioning

confidence: 99%

Repetitive Control to Improve Users’ Thermal Comfort and Energy Efficiency in Buildings

2018

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“…Recently, several researches has utilized ILC as control algorithms for specific EMS with or without employing MAS framework. In what follows, we briefly summarize recent results [8,9,10,11,12,13] and introduce some research directions on designing ILC law for EMS described as MAS.…”

Section: Iterative Learning Control and Applications To Emsmentioning

confidence: 99%

“…There are several interesting studies including [10] and [12] which have great potential of ILC application to EMS. While [10] only considers an office building with a single zone represented by an electric-analogous 3R2C model, [12] investigates multi-room buildings whose models are described by RC model. Both researches employ learning controllers together with feedback controllers to improve the tracking performance under repetitive disturbances.…”

Section: Iterative Learning Control and Applications To Emsmentioning

confidence: 99%

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2016

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This paper presents a brief survey of recent works on Iterative Learning Control (ILC) of Energy Management System (EMS) based on a framework of Multi-Agent System (MAS). ILC is a control methodology which is especially suitable for dynamical systems whose control tasks are executed in a finite time interval and are repeated over and over. The key idea of ILC is to take available system information in the past and current runs, to generate the control input for the next run. EMS is a computer-based system to monitor energy consumption, control operation, and optimize energy supplies and demands. EMS can be naturally modeled as MAS since each power-generated or powerconsumed component of EMS can be cast as agent. Each agent of MAS is a dynamical system itself and has its own target such as tracking desired trajectory and minimizing energy. Moreover, there are common objectives of EMS which aim to attain its energy efficiency, reliability and optimality. Then one agent can cooperate with other agents to achieve some global objectives, in addition to their own local goals, by exchanging information with other agents. Lastly, we will explore some open research problems and their potential applications. Keywords:Iterative learning control (ILC), energy management system (EMS), multi-agent system (MAS), building temperature control (BTC).

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“…This is also when there is customer activity and it gives a high increase in heat load and will be modeled in the simulations conducted in Section V. Furthermore, in order to evaluate the thermal storage potential of different foodstuffs, different combinations of mass times specific heat mC p and overall heat transfer coefficient times surface area U A are used in the simulated display cases. These parameters determine the main time constant for the foodstuff temperature and a step in air temperature is made from 3.5 • C to 1 • C using (13) to determine how long it takes to precool the foodstuff from the initial value to 90% of the step size. The time is 166 min for Display Case 1, 250 min for Display Case 2, 500 min for Display Case 3, and 300 min for the cold storage room.…”

Section: E Foodstuff Storage Potential Considerationsmentioning

confidence: 99%

“…The repeatability can be checked by calculating the repeatable-tononrepeatable ratio for different frequencies as done in e.g. [12], [13]. However, this is not included in this paper.…”

Section: Introductionmentioning

confidence: 99%

A learning based precool algorithm for utilization of foodstuff as thermal energy storage

Vinther

Rasmussen

Izadi‐Zamanabadi

et al. 2013

2013 IEEE International Conference on Control Applications (CCA)

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Maintaining foodstuff within predefined temperature thresholds is important due to legislative requirements and to sustain high foodstuff quality. This is achieved using a refrigeration system. However, these systems might not be dimensioned for hot summer days or possible component performance degradation. A learning based algorithm is proposed in this paper, which precools the foodstuff in an anticipatory manner based on the saturation level in the system on recent days. The method is evaluated using a simulation model of a supermarket refrigeration system and simulations show that thermal energy can be stored in foodstuff to cope with saturation in refrigeration equipment. Additional hardware or a system model is not required, making it easy to implement the method in existing systems.

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Learning/repetitive control for building systems with nearly periodic disturbances

Cited by 7 publications

References 14 publications

Repetitive Control to Improve Users’ Thermal Comfort and Energy Efficiency in Buildings

Repetitive Control to Improve Users’ Thermal Comfort and Energy Efficiency in Buildings

Untitled

A learning based precool algorithm for utilization of foodstuff as thermal energy storage

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