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@PHDTHESIS{Koschwitz:782653,
author = {Koschwitz, Daniel},
othercontributors = {van Treeck, Christoph Alban and Nytsch-Geusen, Christoph},
title = {{A}utoadaptives prädiktives {M}odell zur {Q}uantifizierung
von {G}leichzeitigkeitsfeffekten in {L}astverteilungen
urbaner {E}nergiesysteme},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2020-01982},
pages = {1 Online-Ressource (ix, 238 Seiten) : Illustrationen,
Diagramme},
year = {2020},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2020},
abstract = {The present work describes an auto-adaptive predictive
model for quantifying simultaneity effects within load
distributions of urban energy systems named AMSA
(Auto-adaptive Model for Simultaneity Analysis). The
mathematical structure of AMSA is based on Machine Learning
techniques in combination with methods related to energy
data analysis enabling the integration in intelligent
interconnected energy information systems. AMSA is
implemented in Matlab using a modular design including
different functional components. Thus, individual
modifications of interface functions and modular extensions
do not influence the numerical calculation core. Previous
simultaneity factor quantification for economically
optimized dimensioning of energy generation plants and
networks is based on experience values and empirical
analysis focusing on historical measurement data.
Conventional derivations of characteristic curves using
measurement databases imply a decreasing simultaneity factor
in combination with increasing building group sizes, which
isused for replanning energy supply systems. In order to
optimize the latter, it is necessary to know historical and
current as well as scenario based future energy demand of
single buildings and building groups taking uncertainty into
account. Moreover, similarity analysis concerning building
load profiles is required to recognise simultaneously
recurring patterns in order to identify energy network
systems. In this context, the developed model serves for
knowledge gain from varying complex databases to identify
decision ranges for long- and medium-term planning regarding
the development of districts and urban regions. Furthermore,
on operational level, it improves short-term load management
strategies concerning energy supply. The derivation of
suitable methods for component model development is based on
appropriate scientific literature research and analysis
regarding method categories with their specific
characteristics as well as findings from application-based
studies. An ensemble model serves to calculate future load
conditions, consisting oft wore current neural networks of
various depth and two support vector machine configurations
using different kernels. According to input data
characteristics, a building-specific suitable prediction
method is automatically chosen based on an error evaluation
during the method testing period. In order to analyse
influences of varying weather conditions and building
retrofits on simultaneity effects within urban load
distributions, mathematical-statistical methods for load
profile modifications are introduced. For pattern
recognition of load distributions, self organizing maps
combined with learning vector quantification are used, which
is assigned to competitive neural networks category. In this
regard, initially, buildings are automatically grouped
according to their characteristic load profile. Secondly,
the building group is identified, which provides the most
correlated load profile compared to the district load
profile. Subsequently, simultaneity factors and peak load
shares of buildings an building groups are calculated. In
the final part of this work, monitoring data of
building-specific heating and cooling consumption of a
research campus serve for model demonstration. In this
context, besides statistical analysis of the database,
results of preliminary studies concerning component models
are presented. These include clustering of thermal load
profiles, a detailed comparison concerning prediction models
used as well as long-term heating load predictions based on
retrofit scenarios. Furthermore, conventionally derived
curves for simultaneity factors enable classification and
evaluation of the results using AMSA. Taking the corporate
model level into consideration, various model-related
advantages may be shown: Regarding short-term load
management, less generating capacities have to be provided.
Regarding similarity-based grouping, key buildings and the
building group may be identified, where temporal peak load
shifting contains the largest share of the expected peak
load on district level. This information enables predictive
loading and unloading strategies for storage systems in
terms of demand side management. With respect to longer-term
planning periods, it is shown that simultaneity factors
combined with similarity-based building classification suit
for the identification of possible energy network systems.
Future weather and retrofit scenario analysis demonstrates
external influences on the results of simultaneity analysis,
which emphasizes the demand for an auto-adaptive predictive
model. The scope of future research work should focus on the
validation of AMSA taking detailed information bases into
account},
cin = {312410},
ddc = {624},
cid = {$I:(DE-82)312410_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2020-01982},
url = {https://publications.rwth-aachen.de/record/782653},
}
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