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@PHDTHESIS{Kmpel:1011646,
author = {Kümpel, Alexander},
othercontributors = {Müller, Dirk and Abel, Dirk},
title = {{A}daptive agentenbasierte modellprädiktive {R}egelung
für {G}ebäudeenergiesysteme; 1. {A}uflage},
volume = {140},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {E.ON Energy Research Center, RWTH Aachen University},
reportid = {RWTH-2025-04636},
isbn = {978-3-948234-54-6},
series = {E.On Energy Research Center},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Druckausgabe: 2025. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University; Dissertation,
RWTH Aachen University, 2024},
abstract = {Reducing energy consumption and greenhouse gas emissions in
the building sector requires energy efficient operation of
buildings. One promising method is model predictive control
(MPC), which uses a mathematical model to determine the
optimal mode of operation. The advantages of MPC are
predictive operation, exploitation of flexibility,
multi-variable control, and consideration of multiple
targets. The development of a suitable model, tuning, and
implementation of MPC are expensive and inhibit its
widespread use in the building sector. The objective of this
work is to develop a self-adjusting model predictive control
through an adaptive and modular approach, which can be
applied to various building energy systems with low
configuration efforts, to reduce the barriers of MPC for
practical use. The basic idea of the approach is to divide
the energy system into recurrent subsystems. A hierarchical
agent-based approach is used to control the subsystems,
where agents control the subsystems using adaptive MPC.
Adaptive MPC allows the agents to be transferred to
subsystems of the same type with little configuration
effort. For efficient operation of the overall system, a
coordinator is used to solve a high-level optimization
problem. The optimization problem is modular, analogous to
the agents, and based on a heat flux-based approach. The
specific cost function and model equations of the
optimization problem are determined by the individual agents
and given to the coordinator. The coordinator determines the
setpoints for the individual subsystems by solving the
high-level optimization problem and passes them to the
agents. A simulation model based on a real building energy
system is developed to evaluate the developed control. For a
realistic evaluation, the model is calibrated and validated
with measurement data. Further, the control of the building
energy system is implemented as a reference. In comparison
to the reference control, the developed approach leads to a
higher energy efficiency and an improved quality of control
for the control of various subsystems. Furthermore, the
agents are applicable to subsystems of the same type. The
agent-based control of the overall energy system results in
cost savings up to $59\%$ while reducing the thermal
discomfort by $88\%.$ To demonstrate the transferability to
real systems, the adaptive and agent-based control is
applied to a test hall. The control leads to analogous
behavior as in the simulation and shows the applicability of
the concept in practice.},
cin = {419510 / 080052},
ddc = {620},
cid = {$I:(DE-82)419510_20140620$ / $I:(DE-82)080052_20160101$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2025-04636},
url = {https://publications.rwth-aachen.de/record/1011646},
}
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